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GitHub CEO: manual coding remains key despite AI boom

(www.techinasia.com)
358 points andrewstetsenko | 2 comments | 23 Jun 25 20:50 UTC | HN request time: 0.525s | source
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mycocola ◴[23 Jun 25 21:56 UTC] No.44360585[source]▶
I think most programmers would agree that thinking represents the majority of our time. Writing code is no different than writing down your thoughts, and that process in itself can be immensely productive -- it can spark new ideas, grant epiphanies, or take you in an entirely new direction altogether. Writing is thinking.

I think an over-reliance, or perhaps any reliance, on AI tools will turn good programmers into slop factories, as they consistently skip over a vital part of creating high-quality software.

You could argue that the prompt == code, but then you are adding an intermediary step between you and the code, and something will always be lost in translation.

I'd say just write the code.

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sothatsit ◴[23 Jun 25 22:24 UTC] No.44360816[source]▶
I think this misses the point. You're right that programmers still need to think. But you're wrong thinking that AI does not help with that.

With AI, instead of starting with zero and building up, you can start with a result and iterate on it straight away. This process really shines when you have a good idea of what you want to do, and how you want it implemented. In these cases, it is really easy to review the code, because you knew what you wanted it to look like. And so, it lets me implement some basic features in 15 minutes instead of an hour. This is awesome.

For more complex ideas, AI can also be a great idea sparring partner. Claude Code can take a paragraph or two from me, and then generate a 200-800 line planning document fleshing out all the details. That document: 1) helps me to quickly spot roadblocks using my own knowledge, and 2) helps me iterate quickly in the design space. This lets me spend more time thinking about the design of the system. And Claude 4 Opus is near-perfect at taking one of these big planning specifications and implementing it, because the feature is so well specified.

So, the reality is that AI opens up new possible workflows. They aren't always appropriate. Sometimes the process of writing the code yourself and iterating on it is important to helping you build your mental model of a piece of functionality. But a lot of the time, there's no mystery in what I want to write. And in these cases, AI is brilliant at speeding up design and implementation.

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1. weatherlite ◴[25 Jun 25 09:09 UTC] No.44375145[source]▶
> So, the reality is that AI opens up new possible workflows. They aren't always appropriate. Sometimes the process of writing the code yourself and iterating on it is important to helping you build your mental model of a piece of functionality. But a lot of the time, there's no mystery in what I want to write. And in these cases, AI is brilliant at speeding up design and implementation.

I agree but I have a hunch we're all gonna be pushed by higher ups to use AI always and for everything. Headcounts will drop, the amount of work will rise and deadlines will become ever so tight. What the resulting codebases would look like years from now will be interesting.

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2. sothatsit ◴[25 Jun 25 11:53 UTC] No.44376217[source]▶
Yeah, I am grateful that I work with a lot of other engineers and managers who care a lot about quality. If you have a manager who just cares about speed, the corner cutting that AI enables could become a nightmare.