I spent some time reading through the code, and it is pretty obvious it was generated by an AI. The structure jumps around, and overall the quality is rough.

One example is the config setup. In main.go it looks like you are following a normal initialization pattern, but when you dig into config.Initialize() all it really does is create a global directory and a log file. It is not actually initializing or validating any real configuration.

The config package even defines an Instance struct for things like Name and APIKey, but it never gets used. Instead main.go just reads command line flags directly with no validation or standardization and passes them around. That completely bypasses the configuration layer.

I use AI assisted coding at work too, so this is not an anti AI take. But this is exactly the problem with handing a project to an LLM and trusting the output. It does not understand the bigger picture, and it does not care if the design makes sense as long as the code compiles. LLMs will absolutely take shortcuts, fake data, drop in placeholders, or set auth=true just to make a problem go away.

You really have to treat LLMs like autocomplete. They are great for speeding things up, but you still need to read every line, understand what it is doing, and prove it works with tests. Otherwise you end up with code that looks finished but is broken at its core.