Sorry, I guess. There were no comments here after a couple of hours and I literally felt bad, so I commented without being very exact in my writing. Regardless, great job, and thank you.
It is SO NICE to see people working on making fast, nice-to-use tools. It's a lovely experience to use diffshub. Thank you for creating it, and than you for the great write-up! (I made it "that far" )
Have you split your 100k loc codebases into smaller crates? If you take a look at eg gitoxide's repo, they've split it in many smaller crates. I think that might help with keeping the scope for the ai small and maybe help with keeping contracts tight and well-defined.
You could probably use just an X-scanner, and instead of a CCD line sensor, use a regular 2D image sensor if you used a "1 pixel wide" slit aperture to crop the image perpendicularly to the direction that the prism disperses the light. So instead of a single pixel being dispersed, you disperse a line.
You would reduce the time required by the root of the number of pixels you want (assuming a square image).
(This is what we do in momentum-resolved electron energy loss spectroscopy. In that situation we have electromagnetic lenses that focus the electrons that have been dispersed, so we don't have as bad a chromatic aberration problem as the other response mentions).
I would love to see e.g. a butterfly image with a slider that I could drag to choose the wavelength shown!!
This is called chromatic aberration, for those who are intrigued.
Given that regular phone cameras have sensors that detect RGB, I wonder if one could notice improved image sharpness if one had three camera lenses (and used single-color sensors) next to one another laterally, with a color filter for R, G and B for each one respectively. So that the camera could focus perfectly for each wavelength.
This is very interesting, but like sibling comments, I'm very curious as to how you run this in practice. Do you just tell Claude/Copilot to do what you describe?
I used it to map out storage locations and refill stations at our online grocery picking stations, then export it to read in using geopandas in order to calculate the shortest distances between all locations!
I get it but I think you're essentially saying "I learnt it this way and it's hard to change once I learnt it that way". My argument is that "when teaching others, it's better to teach them to use switch and restore rather than checkout".
I'd recommend using git switch instead of checkout, since the checkout command is so overloaded. And restore instead of checkout for restoring changes.
Any suggestions for «orchestrating» this type of experiment?
And how does one compare the results in a way that is easy to parse? 7 models producing 1 PR each is one way, but it doesn’t feel very easy to compare such.
You need something "more" on the website before you ask people to create an account. "Team workspace that stays fast" isn’t clear enough for me, at least. What is a workspace? What does the interface look like? Is it in the browser? Is it an app?
People will go "what is this?", "huh, I’m not gonna make a user for this, can’t tell what it is". Those are my 2 cents.
Haha, I started reading this, got interrupted, came back and got confused by the graph. Then came to the comments, saw your comment, reloaded the post and voila!