I spent weeks reading everything YC has ever published about what they look for.
here are 9 things that would have changed how I thought about the application
@paulg Context is important more than explanation...
all LLMs are designed to provide lots of explanation which is not needed,
Verbose needs to be removed from all LLMs
Many people think any given ML project is 99% training.
In reality, it’s 50% evaluation, 40% data cleaning, 8% integration, and 2% training.
The first two set the noise floor for learning. No ML magic matters; the model cannot lower the noise floor, as that’s the optimal bound of Shannon encoding of your data.
Thus, not a single day goes by without me thinking about ontology. Even the old labels have to be constantly reviewed.
@paulg It's called the "distrust tax"
& it forces extra mental effort to second guess colleagues actions, check for errors/deception & consider worstcase scenarios, inflating cognitive load and slowing everything down
Damn, i read lots of books....