Hi Jonathan
On 2026/02/20 15:39, Jonathan Dowland wrote:
For example, a few weeks ago I was working late one night and I
couldn't put my finger on it, but my one loop just looked really wrong
and ugly, so I searched on duck duck go to go find some patterns that
look nice that fit my use case, and Duck Duck AI popped up and
suggested a very neat and elegant list comprehension that was such an
obviously good choice, that I really should have thought of it in the
first place.
Personally, as part of refining my own position on these matters, I've
wanted to explore the idea of what would be acceptable to me, wrt
copyright, to harness the value you demonstrate in your anecdote.
An LLM which was solely trained on a corpus of free software with intra-
compatible licensing (for the sake of this example say, GPL2 or later,
and anything compatible with it), such that we declare the resulting
weightings to be a derivative, licensed GPL2+, and attribute the
authorship to the union of authorship of *all* the inputs, and consider
anything it outputs to be a derivative, likewise GPL2+. Would that be
acceptable? Would that be useful?
IMHO that would be useful, especially if it could be accurate enough
with the attribution part. Does such a model exist yet?
-Jonathan
