It uses an experimental compiler plugin for the Scala compiler. It's typesafe at compile time. At runtime unfortunately it relies on exceptions for control flow.
1. Train one ML implementation to produce "specification text" in a way that they're agreed to be free from copyright claims. E.g. train to avoid any direct quoting, possibly via a different human, programming or custom specification language.
2. Train a separate ML implementation to produce code from the specifications.
3. Hook them together and you've got a pipeline for generating learned, but copyright-free, code.
Kind of reminds me a bit of some of the machine translation work with human languages.
Note: this is how the GNU project itself sometimes clones the functionality of copyright-free way, so I'm pretty sure it would be safe to use this on GPL-licensed code.
They're equivalent plans, but people have cognitive biases. Here are a couple of explanations from Propspect Theory:
- refunds are perceived as gains (against the reference point of the monthly charge), whereas a fee is perceived as a loss (and losses hurt more)
- bills are capped so the the possibility of big fees (losses) is eliminated (people struggle with probabilities and fear extremely unlikely events)
https://rd.nz/2009/03/goto-in-scala.html
It uses an experimental compiler plugin for the Scala compiler. It's typesafe at compile time. At runtime unfortunately it relies on exceptions for control flow.