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421 points briankelly | 1 comments | | HN request time: 0.34s | source
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conductr ◴[] No.43576495[source]
As a long time hobby coder, like 25 years and I think I’m pretty good(?), this whole LLM /vibecoding thing has zapped my creativity the past year or so. I like the craft of making things. I used tools I enjoy working with and learn new ones all the time (never got on the JS/react train). Sometimes I have an entrepreneur bug and want to create a marketable solution, but I often just like to build. Im also the kind of guy that has a shop he built, builds his own patio deck, home remodeling, Tinker with robotics, etc. Kind of just like to be a maker following my own creative pursuit.

All said, it’s hard on me knowing it’s possible to use llm to spit out a crappy but functional version of whatever I’ve dreamt up with out satisfaction of building it. Yet, it also seems to now be demotivating to spend the time crafting it when I know I could use llm to do a majority of it. So, I’m in a mental quagmire, this past year has been the first year since at least 2000 that I haven’t built anything significant in scale. It’s indirectly ruining the fun for me for some reason. Kind of just venting but curious if anyone else feels this way too?

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carpo ◴[] No.43576813[source]
I'm the complete opposite. After being burnt out and feeling an almost physical repulsion to starting anything new, using AI has renewed my passion. I've almost finished a side project I started 4 weeks ago and it's been awesome. Used AI from the beginning for a Desktop app with a framework I'd never heard of before and the learning curve is almost non-existent. To be able to get the boring things done in minutes is amazing.
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crm9125 ◴[] No.43577965[source]
Similar sentiment here. I taught myself python a decade ago after college, and used it in side projects, during my masters degree, in a few work projects. So it's been handy, but also required quite a bit of time and effort to learn.

But I've been using Claude to help with all kinds of side projects. One recently was to help create and refine some python code to take the latest Wikipedia zipped XML file and transform/load it locally into a PostgreSQL DB. The initial iteration of the code took ~16 hours to unzip, process, and load into the database. I wanted it to be faster.

I don't know how to use multiple processes/multi-threading, but after some prompting, iterating, and persistent negotiations with Claude to refine the code (and an SSD upgrade) I can go from the 24gb zip file to all cleaned/transformed data in the DB in about 2.5 hours. Feels good man.

Do I need to know exactly what's happening in the code (or at lowers levels, abstracted from me) to make it faster? not really. Could someone who was more skilled, that knew more about multi-threading, or other faster programming languages, etc..., make it even faster? probably. Is the code dog shit? it may not be production ready, but it works for me, and is clean enough. Someone who better knew what they were doing could work with it to make it even better.

I feel like LLMs are great for brainstorming, idea generation, initial iterations. And in general can get you 80%+ the way to your goal, almost no matter what it is, much faster than any other method.

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1. carpo ◴[] No.43578673[source]
That's awesome! That's a lot of data and a great speed increase. I think that as long as you test and don't just accept exactly what it outputs without a little thought, it can be really useful.

I take it as an opportunity to learn too. I'm working on a video library app that runs locally and wanted to extract images when the scene changed enough. I had no idea how to do this, and previously would have searched StackOverflow to find a way and then struggled for hours or days to implement it. This time I just asked Aider right in the IDE terminal what options I had, and it came back with 7 different methods. I researched those a little and then asked it to implement 3 of them. It created an interface, 3 implementations and a factory to easily get the different analyzers. I could then play around with each one and see what worked the best. It took like an hour. I wrote a test script to loop over multiple videos and run each analyzer on them. I then visually checked the results to see which worked the best. I ended up jumping into the code it had written to understand what was going on, and after a few tweaks the results are pretty good. This was all done in one afternoon - and a good chunk of that time was just me comparing images visually to see what worked best and tweaking thresholds and re-running to get it just right.