**#A quick edit to address something important and provide a disclaimer: **
Thank you all for your feedback! This project was “vibecoded” with Cloude AI and serves more as a “proof of concept” for what could be achieved with AI assistance. I’m just a tech enthusiast, and I’m excited to continue exploring new possibilities. I understand there’s a real concern about “AI Slop,” but that’s exactly why I’m sharing this project with you all so that experts who are interested in the idea can offer guidance or even help improve it.
I’ve noticed that many people with home labs prefer to update their applications manually instead of relying on other apps that automate the process. Often, they have to check each one individually. That’s where Vigil comes in. The primary function of Vigil is to centralize the information and give users clear visibility of which applications are outdated, their current version, and the newer version available from several sources. This way, you can decide what and when to update.
To be honest, I hope it ends up being useful to others as it is for me.
If you have a few minutes, I’d really appreciate you trying it out and leaving a review or suggestions on the repo or even here. I’d do my best to answer most of the comments.
An issue with your statement “know what you’re doing by doing it” is that without an actually educated teacher to provide trustworthy feedback, you are going to struggle the learn from your mistakes. The LLMs can only provide so much, and they will lie out their ass to you. Unless explicitly prompted to provide critical feedback, they will find any way to provide positive feedback even to your actual detriment. They will happily skirt their sandboxes, and fight your every attempt to make them actually safe.
At a quick glance, nothing in the project indicates that you are not an expert and that an AI Agent provided the code. The quality of the code is also quite poor, even by Claude standards. I’m actually kinda mind blown you got it to built this without any tests… Something we’ve recently been talking about at my job in terms of AI agents is “cognitive debt” that is incurred in the project. LLMs are fundamentally a statistical next-word generator. If they are given something of poor quality, they will tend to produce more and more poor quality work. And without intervention, it just snowballs.
I’ll never tell someone to stop trying to learn. But, your hubris is going to negatively impact your learning outcomes. And to be clear, YOU are not writing the code and the code is what runs on the server and people interact with. What you are doing is using an AI Agent. If you want to get feedback on that, then be honest about it.
Hi ramielrowe. You made great points here. I’m definitely stepping out of my area of expertise. I also understand that when comes to LLM we must not blindly follow/accept things and having some previous knowledge on the topic you intend to work with, gives you much better results and allow you to spot inconsistencies or more importantly, mistakes. I’m aware of the “positive feedback” that is pretty evident specially on ChatGPT, that’s why I try my best to challenge it. I completely understand your analogy on “cognitive debt”. It’s pretty similar to a reinforcing learning process on humans. If you teach people the “wrong way” and keep reinforcing that without any correction, you know the results.
Regarding the code quality, I’m pretty sure it isn’t top notch, that’s why I’m sharing it here so people who really understand it can point out the flaws and suggest improvements. What I’ve learned so far from the feedback in the comments, is that I need to improve the way I communicate my ideas and the purpose of the application. Since this is my first project, and I’m not very familiar with the dev & tech community, I’m learning the do’s and don’ts along the way.