"We follow a strict 5-phase discipline" - So we're doing waterfall again? Does this seem appealing to anyone? The problem is you always get the requirements and spec wrong, and then AI slavishly delivers something that meets spec but doesn't meet the need.
What happens when you get to the end of your process and you are unhappy with the result? Do you throw it out and rewrite the requirements and start from scratch? Do you try to edit the requirements spec and implementation in a coordinated way? Do you throw out the spec and just vibe code? Do you just accept the bad output and try to build a new fix with a new set of requirements on top of it?
(Also the llm authored readme is hard to read for me. Everything is a bullet point or emoji and it is not structured in a way that makes it clear what it is. I didn't even know what a PRD meant until halfway through)
In other words, for 95% of people doing activity, they shouldn't eat any surplus if their goal is to maintain or lose weight.
It's actually best to do most of your activity undernourished, as it helps develop true intuitive nutrition feedback sensation. You'll start to sense how every macro and salt feels when you ingest it. Loss of this sensation is a major obesity driver. A numbness for nutrients.
If AI at least equal humans in all intellectual fields then they are super-intelligences, because there are already fields where they dominate humans so outrageously there isn't a competition (nearly all fields, these days). Before they are superintelligences there is a phase where they are just AGIs, we've been in that phase for a while now. Artificial superintelligence is very exciting, but Artificial non-super Intelligence or AGI is here with us in the present.
To me, superintelligence means specifically either dominating us in our highest intellectual accomplishments, i.e. math, science, philosophy or literally dominating us via subordinating or eliminating humans. Neither of these things have happened at all.
One of the benefits of using AI is that these processes, which I personally never followed in the pre-AI era, are now easy and frictionless to implement.