Paul Werbos did not apply backprop to MLPs as cleanly described in Hinton's paper, but rather to some kind of autoregressive non-linear parametrized functions with a much more specific application scope.
Both papers are direct applications of the chain rule applied to estimate the gradient of a multivariate function.
How do you deny access to prod credentials from an assistant running on your dev machine assuming you need to store them on that same machine to do manual prod investigation/maintenance work from that machine?
When you run Antigravity the first time, it asks you for a profile (I don't remember the exact naming) and you what it entails w.r.t. the level of command execution confirmation is well explained.
I think there is far less than 1% chance for this to happen, but there are probably millions of antigravity users at this point, 1 millionths chance of this to happen is already a problem.
We need local sandboxing for FS and network access (e.g. via `cgroups` or similar for non-linux OSes) to run these kinds of tools more safely.
Personally, I do not understand why you think there is a bug from this screen capture alone. Maybe because I am that familiar with penpot and figma, but still, I do not find it obvious.
This is why it's important to describe explicitly the three points in text:
- steps to reproduce;
- what you expected to happen;
- what actual result you observe instead.
Something that might be obvious to you but isn't for others will just be silently ignored most of the time.
EDIT: I now see the problem after reading your other reply above:
This is why it's important to describe explicitly the difference between what you expected and what you observed. I swear I did not see the change in button width before reading the linked comment.
I think it would help to open an issue on github making explicit the following three points explicit in the report:
- steps to reproduce from scratch;
- what you expected to happen;
- what you actually observed (include the screenshot or video capture in addition to a textual description).
Otherwise, you might risk your report being ignored due to a silent misunderstanding about the mismatch between your expectations and the actual results.
Both papers are direct applications of the chain rule applied to estimate the gradient of a multivariate function.