I wish more people understood this.

Do I use LLMs to write software for my personal use? Sure. I still try to build it in an “incremental” way the same way I would write software manually so I don’t get 10k SLOC written in a week, but at some point, even reviewing 100 LOC changes takes time, so I just take a cursory look at the diff, yolo-merge-and-run to test it. It’s not critical. This is fine. I’m just exploring the problem domain and solutions.

But would I go as far as sharing it, making a damn git repo and advertising it on Lemmy? Fuck no. This is unreliable, inscrutable slopware tailored for my own use. Anyone with a local LLM or a 20 euro claude/codex/z.ai subscription can do the same thing in a few minutes of work a day.

A single 10-line patch/contribution to a human-written project, with contributors who understand the code, or even a well-curated comment in a bug tracker that helps devs debug an issue or clearly expresses a need, has more value for the community than 50 vibe-coded projects.

I didn’t find one that would work entirely authelssly and which would allow negative entries

Have you considered filing feature requests for these on existing, well-maintained projects?

Yes, even writing a proper issue report probably entails more work and brainstorming than prompting your way to a shitty solution. No offense meant, I do it as well. I know using a LLM and pumping out a working solution to a complex problem in a week gives a feeling of euphoria and power; this wouldn’t have been possible at all a few years ago. But there is absolutely no value in proactively sharing and advertising it.

I appreciate OP being transparent about it though.

Yep. https://lemmy.world/post/46066942/23416719 Basic setup, works for me

they don’t go out of their way to make the self hosted option easy

Just follow the docs https://docs.nextcloud.com/server/stable/admin_manual/installation/source_installation.html ? The manual install is a simple webserver + PHP-FPM + postgres setup

I have automated it with an ansible role [1], there’s nothing complicated about it, really.

I suggest using llama.cpp instead of ollama, you can easily squeeze +10% in inference speed and other memory optimizations from llama.cpp. With hardware prices nowadays I think every % saved on resources matters. Here is a simple ansible role to setup llama.cpp, it should give you a good idea of how to deploy it.

A dedicated inference rig is not gonna be cheap. What I did, since I need a gaming rig; is getting 32GB DDR5 (this was before the current RAMpocalypse, if I had known I would have bought 64) and an AMD 9070 (16GB VRAM - again if I had known how crazy prices would get I’d probably ahve bought a 24GB VRAM card). The home server runs the usual/non-AI stuff, and llamacpp runs on the gaming desktop (the home server just has a proxy to it). Yeah the gaming desktop has to be powered up when I want to run inference, this is my main desktop so it’s powered on most of the time, no big deal