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I've created hundreds of small scripts that I wouldn't have bothered with before and either do some manual checks or just not possess the information. Just on "
by vasco 1y ago
I've created hundreds of small scripts that I wouldn't have bothered with before and either do some manual checks or just not possess the information. Just on "small script" productivity it already saved me a lot of time.
The problem is people trying to make the models do things that are too close to their limit. You should use LLMs for things they can ace already, not waste time trying to get it to invent some new algorithm. If I don't 0-3 shot a problem then I will just either do it manually or not do it.
Similarly to giving up on a Google search that you try a few times and nothing useful comes in the first few prompts. You don't keep at it the whole afternoon.
- paulcole 1y agoI'm in a similar same boat as you. I'm not a programmer but from time to time would make automations or small scripts to make my job easier. LLMs have made much more complex automations and scripts possible while making it dead simple to create the scripts I used to make.
- timeinput 1y agoAgreed, and after you've worked with them for a bit you can start to predict where they are going to fail, and do something silly like delete your code base, or have no trouble, and succeed, and add that feature you were after. A good code review and edit to remove the excess verbosity, and you got a feature done real fast. Ask it for something at or above its limit then the code is very difficult to debug, difficult to understand has potentially misleading comments, and more. Knowing how to work with these overly confident coworkers is definitely a skill. I feel it varies from significantly from model to model as well. Its often difficult to task other programmers with tasks at or above their limits too.
- gspencley 1y agoYeah I'm not an AI naysayer but was pretty skeptical that AI could actually do anything for my workflow. Tedious, repetitive stuff that you'd typically write a throwaway script for is one good use case for AI for me. This doesn't come up very often for me, however. But this morning I needed to update a bunch of import statements in TypeScript files to use relative paths instead of referencing a library name and Cursor did that for me quickly and easily without me needing to write a script. I've also found that if you're unsure how to solve a problem within an existing codebase, or you're about to go down a rabbit hole of studying documentation to figure out how to wire something up, an LLM can get you up and running with code examples a bit quicker. But if I already know, high level, how I'm going to solve something then the whole "needing to review the AI code" part of the workflow more than eats up any time savings. And the parallelism thing is just a ton of context shifting to me. Each time I need to go back to an agent to review what it did, it takes me away from the deep focus on a domain problem that I was focused on, and there's a good 20 - 30 minutes of productivity lost by switching back and forth between just two tasks.
- lucianbr 1y ago> The problem is people trying to make the models do things that are too close to their limit. As advertised or at least strongly implied by the companies that own the models.