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170 points mogambo1 | 2 comments | | HN request time: 0.018s | source
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OptionOfT ◴[] No.45290784[source]
And the value of AI as pushed to us by these companies is in doing larger units of work.

But... reviewing code is harder than writing code. Expressing how I want something to be done in natural language is incredibly hard.

So over time I'm spending a lot of energy in those things, and only getting it 80% right.

Not to mention I'm constantly in this highly suspicious mode, trying to pierce through the veil of my own prompt and the code generated, because it's the edge cases that make work hard.

The end result is exhaustion. There is no recharge. Plans are front-loaded, and then you switch to auditing mode.

Whereas with code you front-load a good amount of design, but you can make changes as you go, and since you know your own code the effort to make those are much lower.

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1. rjh29 ◴[] No.45298898[source]
Don't spend energy on clever prompting and reviewing. Spend your energy on knowing when to use the LLM at all.

If you know what to write but it's tedious, LLMs are great, they'll just fill all that in for you. Anything more complex or open that needs checking could be quicker to just think through and write yourself. You can still use LLMs at the edges, e.g. what API methods should I use for this?

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2. RealityVoid ◴[] No.45299420[source]
I like using LLM's for exploration of a new codebase, seeing what other options already exist for solving a solution I might not be aware of and planning a bit how to change stuff, since it sometimes sees thing I did not think of.

Another thing it's good at is writing tests - a lot of times I won't bother, but with AI I can do it cheaply. And it's very good at keeping documentation and a codebase consistent, believe it or not. If I change a part that is mentioned somewhere else and it has it in the context window, it will update both parts, whereas I might omit it.