[Foreignborn]

I have a team that’s somewhat junior at a big company. We pretty much have everyone “vibe plan” significantly more than vibe code.

- you need to think through the product more, really be sure it’s as clarified as it can be. Everyone has their own process, but it looks like rubber ducking, critiquing, breaking work into phases, those into tasks, etc. (jobs to be done, business requirement docs, domain driven design planning, UX writing product lexicon docs, literally any and all artifacts)

- Prioritize setting up tooling and feedback loops (code quality tools of any and every kind, are required). this includes custom rules to help enforce anything you decided during planning. Spent time on this and life will be a lot better for everyone.

- We typically making very very detailed plans, and then the agents will “IVI” it (eg automatic linting, single test, test suite, manual evaluation).

You basically set up as many and as diverse of automatic feedback signals as you can.

—-

I will plan and document for 2-4 hours, then print a bunch of small “PRDs” that are like “1 story point” small. There’s clear definitions of done.

Doing this, I can pretty much go the gym or have meetings or whatever for 1-2 hours hands off.

—-

[lionkor]

I pray for whoever has to review code you didn't bother writing

[lubujackson]

I think this is a good use of AI. Change your thinking - the code is, and has always been, a medium between the computer and the human. Where is the human? Where do we define our intent? AI gives us a chance to redefine that relationship or at least make it more fluid.

A well-architected system is easier to develop and easier to maintain. It makes sense to put all the human effort into producing that because, lo and behold, both humans and LLMs can produce much better results within a well-defined structure.

[lionkor]

LMMs don't follow instructions very well. They lose track of constraints around conversational turns, which makes them extremely unreliable

[Foreignborn]

Everyone is responsible for what they deliver. No one is shipping gluttonous CLs, because no one would review them. You still have to know and defend your work.

Not sure what to tell you otherwise. The code is much more thought through, with more tests, and better docs. There’s even entire workflows for the CI portion and review.

I would look at workflows like this as augmentation than automation.

[mrguyorama]

>Everyone is responsible for what they deliver.

What this actually means is that your manager gets a raise when the AI written code works, and you get fired when it inevitably breaks horribly. You also get fired if you do not use AI written code

[Sammi]

Software is going to be of two types:

1. Mostly written by LLMs, and only superficially reviewed by humans.

2. Written 50-50% by devs and LLMs. Reviewed to the same degree as now.

Software of type 2 will be more expensive and probably of higher quality. Type 1 software will be much much more common, as it will be cheaper. Quality will be lower, but the open question is whether it will be good enough for the use cases of cheap mass produced software. This is the question that is still unanswered by practical experience, and it's the question that all the venture capitalists a salivating about.

[danaris]

I 100% guarantee you there will be plenty of software still written fully by humans—and even more that's written 95% by humans, with minor LLM-based code autocomplete or boilerplate generation.

[LtWorf]

Especially for companies that actually have to deliver a product that works or provide support when it doesn't.

[stpedgwdgfhgdd]

“We typically making very very detailed plans” - this is writing code in English without tests. Admittedly, since generating code is faster, you get faster feedback. Still, I do not think it as efficient as an incremental, test driven approach. Here you can optimize early on for the feedback loop.

[Cthulhu_]

You get faster feedback in code, but you won't know if it actually does what it's supposed to do until it's in production. I don't believe (but have no numbers) LLMs speed up the feedback loop.