alt Hacker NewsAuthor here. I'm surprised to see this surfacing now. I just wanted to clarify, since apparently the post doesn't do a good job at it, that what I discussed there is not a methodology I advocate for. The point of the post was: ok, since there are organizations mandating to maximize speed by reducing time spent on typing code (or even mandating to maximize agents usage), is there a way we can meet that requirement while still preserving the rigor somewhere else?
This was a follow up to a previous article[1] and the pair tried to express what I still think today (using AI daily at work): every time I use AI for coding, to some capacity I'm sacrificing system understanding and stability in favor of programming speed. This is not necessarily always a bad tradeoff, but I think it's important to constantly remind ourselves we are making it.
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> every time I use AI for coding, to some capacity I'm sacrificing system understanding and stability in favor of programming speed.
Sure, but couldn't you say the same for letting other people contribute code too? In either case, you make the choice of how deeply you want to review it. You can ask the AI or the human to explain things that aren't clear.
For me it's case by case in either scenario. Sometimes it's not that important to look closely at a specific subsystem that's self-contained or just simple, other times I need to carefully audit whatever touches a different system. You need a good sense of the existing codebase/architecture in the first place to make these determinations.
It gets better:
(NOT a lawyer)
Previously, liability and indemnification could be bureaucratically laundered to "engineers", because it was a huge diffuse set of people.
Now the bag is left with top of the chain for authorizing LLMs. Gia Tan went the hard way with xz. LLM-trolling is the new social engineering.
Don’t you think that the provider of the LLM is also a dimension on these discussions about responsibility? We often talk about the tech itself (LLM driven development) but how we access it is just as important imo. It’s either locked behind a non trivial amount of hardware (for open models) or some monopolistic driven provider entity like OpenAI or anthropic. In the provider case, it’s not really the LLM that will “own” the code, it’s the provider itself and we’ll be at the mercy of whatever pricing model they shove down our throats.
I don't like the premise of the article, but I agree that if you accept the premise, the contents of the articles are a good way to do it.
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For sure every time you use ai you’re sacrificing understanding if you don’t plan out and understand how exactly the ai is going to do the work you asked it to do.
The same output that is such a bad thing in this article can also be used to gain context, by making a thorough plan with your ai first, reading through the plan and proposing changes just like you would with a real developer.
You can also use this output to have the ai write a journal as well. The journal can be as detailed as possible and essentially a ledger of all of the changes your ai has made to the code. This allows not only for your teammates reviewing your pr to gain greater context, but also can be used by yourself, or even the ai itself to figure the why behind a particular implementation was done the way it was, far into the future even.
Lastly how many of us ever deploy code without actually checking the feature works e2e? I would gather not many of us do, I don’t, because even though we may have a greater understanding of the code, we can make mistakes in the code or in our logic. And I keep coming back to why would we treat llms any differently? I believe we should be spending our energy thoroughly manually testing a feature to make sure when we brainstormed we actually did get every edge case, and it works well.