atomicnature
Joined 3 years ago
Joined 3 years ago
Also another unstated assumption is that both the "author" and "reviewer" are reading AI-generated stuff, at least partly
First of all -- the author should not be surprised by any of the things the AI is reporting. If the author is -- either the reviewer AI went wrong, or the generative component went wrong. Either way -- there is something worth looking at in greater depth.
For the reviewer -- or "traditional reviewer" as we may term them -- they are dealing with much greater volume per day now, again due to AI generation. This is supposed to help them get through things faster while not losing comprehension altogether.
There's a reason we still have the code diff right below -- they are encouraged to check things themselves if they feel "something is off" with the quiz.
The point is -- a few questions hopefully gets the wheels spinning in the reader rather than looking at the diff passively and saying "LGTM"
And yes -- all the review results are grounded strictly in the diff
Higher-level design often involves dissolving apparent tensions or opposites through layering or more sophisticated mechanisms, while preserving the benefits of both.
Example ->
Proposition: "Phew, we have to choose between convenience and security."
Naive design: "Let's find a watered-down compromise."
Good design: "Let's layer security on top of convenience by designing the system so users don't have to choose, or water either one down."
Great companies have a history of dissolving apparent contradictions under a single roof via good design.
Building LiveReview:
https://github.com/HexmosTech/LiveReview
Risk aware AI code review for business-critical systems