A reverse engineer friend once taught me I could patch an x86 function with `0xEBFE` to get the CPU to spin forever. It wasn’t until much later that I understood that (IIRC) 0xEB is the “single byte” jump instruction and that of course 0xFE is -1 as a signed byte. Hence the spin.
Even cooler, let's say you need to "update" a subset of your parquet files after they are written. Once you have your parquet files in a ducklake, you can "virtually" update them (the files themselves aren't touched, just new ones created). Something like:
- create your frozen ducklake
- run whatever "normal" mutation query you want to run (DELETE, UPDATE, MERGE INTO)
- use `ducklake_rewrite_data_files` to make new files w/ mutations applied, then optionally run `ducklake_merge_adjacent_files` to compact the files as well (though this might cause all files to change).
- call `ducklake_list_files` to get the new set of active files.
- update your upstream "source of truth" with this new list, optionally deleting any files no longer referenced.
The net result should be that any files "touched" by your updates will have new updated versions alongside them, while any that were unchanged should just be returned in the list files operation as is.
Not that I expect them to fix it at this point since it seems to be a known issue, but just in case anyone from Notion is watching please fix the macOS app’s CPU usage. On a brand new M3 MacBook Pro each tab takes about 10% of one core _non-stop_ even in the background. I have to constantly cull tabs or my machine becomes noticeably less responsive.
Aha! This post finally made it click for me that that is exactly what’s going on, thanks for putting it so succinctly.
I think one of the big differences between AI and most other previous technologies is that the potential impact different people envision is very high variance, anywhere from extremely negative to positive with almost all points in between as well.
So it’s not just different risk tolerances its also that different people see the risks and rewards very differently.
As other have mentioned I’ve used it for diagrams in code comments and READMEs (before GH added mermaid integration).
Making readable diagrams with 80 character width can be a challenge.
I bought it back in either late 2017 or early 2018 and used it a fair amount at first but will admit it’s been a couple years since and haven’t tried reinstalling since my last clean OS wipe.
Seems like the technique only works in highly controlled scenarios (e.g. you have tens of phenotypes and images and want to have a better than random chance of assigning one to the other):
> Nevertheless, re-identification risk in the wild does not appear to be especially high. While we observe a success rate as high as 25%, this is only achieved when the genomic dataset is extremely small, on the order of 10 individuals. In contrast, success rate for top 1 matching drops quickly and is negligible for populations of more than 100 individuals. Moreover, it should be kept in mind that this result assumes that we can predict the phenotypes perfectly.
That's a good catch. This study uses images for reidentification. I wonder which other factors, not contained in an image could be used for reidentifying someone and by how much that would increase the reidentification accuracy.
I just wish they would make more compact shows that tell their entire story in a single self contained season. If 1899 for example had only been given a single season from the outset it actually might’ve been a much better show.
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