Back in the day when you found a solution to your problem on Stackoverflow, you typically had to make some minor changes and perhaps engage in some critical thinking to integrate it into your code base. It was still worth looking for those answers, though, because it was much easier to complete the fix starting from something 90% working than 0%.

The first few times in your career you found answers that solved your problem but needed non-trivial changes to apply it to your code, you might remember that it was a real struggle to complete the fix even starting from 90%. Maybe you thought that ultimately, that stackoverflow fix really was more trouble than it was worth. And then the next few times you went looking for answers on stackoverflow you were better at determining what answers were relevant to your problem/worth using, and better at going from 90% to 100% by applying their answers.

Still, nobody really uses stackoverflow anymore: https://blog.pragmaticengineer.com/stack-overflow-is-almost-...

You and most of the rest of us are all actively learning how to use their replacement

▲

eudamoniac 2 days ago | parent | next [-]

> it was much easier to complete the fix starting from something 90% working than 0%.

As an expert now though, it is genuinely easier and faster to complete the work starting from 0 than to modify something junky. The realplayer example above I could do much faster, correctly, than I could figure out what the AI code was trying to do with all the effects and refactor it correctly. This is why I don't use AI for programming.

And for the cases where I'm not skilled, I would prefer to just gain skill, even though it takes longer than using the AI.

	▲	weitendorf 2 days ago \| parent [-]
		Anecdotally I think you're right that the more skilled you are at something, the less utility there is for something that quickly but incompletely takes you from 0 to 90% But I would generally be skeptical of anybody who claims that all their work is better off starting from 0, the same way I'd be skeptical of someone who claims to not use or need to make google searches about docs/terms/issues as they work. I'll give you an example of something I understand decently well but get a lot of use out of AI for: bash scripts and unit testing. These are not my core work but they are a large chunk of my work. Without LLMs I would just not write a lot of bash scripts because I found myself constantly looking things up and spending more time than expected getting the script to work across environments / ironing out bugs - I would only write absolutely essential scripts, and generally they'd not be polished enough to check in and share with the team, and just live on my computer in some random location. Now with LLMs I can essentially script in english and get very good bash scripts, so I write a lot more of them and it's easier for me to get them into an acceptable state worth sharing with my team. Similarly, I really like Golang table tests but hate writing all the cases out and dealing with all the symbols/formatting. Now I can just describe all the different permutations I want and get something that I can lightly edit into being good enough. I've also found that with domains I am knowledgable enough about, that can translate into being better at going from ~70% to 95% with AI too. In those cases I am not necessarily using AI the same way as someone trying to go from 0->90%: usually they're describing the outcome/goals/features they want relatively informally without knowledge of the known-unknowns and gotchas involved in implementing that. With more knowledge you can prompt LLMs with more implementation/design details and requirements, and course correct away from bad approaches much faster than someone who doesn't know the shape of what they're trying to do. That still comes in handy a lot of the time. Think about how much time you can save by feeding an API spec/docs into an LLM, telling it create a Go struct for JSON (de)serialization of some monstrous interface like https://docs.cloud.google.com/compute/docs/reference/rest/v1...? Or how much easier it is to upgrade across breaking versions of a language/library when you can just bump the version, note all the places where the old code broke, and have an LLM with an upgrade guide/changelog do all the drudgery of fixing each of the 200 callsites you need to migrate to the next version.

▲

Forgeties79 2 days ago | parent | prev [-]

The difference is you’re generally retooling for your purpose rather than scouring for multiple, easily avoidable screw ups that if overlooked will cause massive headaches later on.