ryanschneider

Semi-related, but I was just thinking today I wonder what sort of performance improvements, if any, one could see by having one of the frontier models build a operating system from scratch that targets the subset of imports WINE uses as the standard library? Basically, build whatever syscalls (or shims) WINE and the open source vulkan drivers need and nothing more.

Probably outside the scope of what an individual could pull off on thier own in a reasonable time, but I could totally see for example Valve considering that approach for a later iteration of SteamOS.

> I trained a 125M model to autocomplete piano on-device

I first parsed the "on device" as _on the piano_ and was intrigued how that worked :) maybe someone can make "dynamic" rolls for a player piano somehow (like one of those braille "screens")?

The trick I learned from a kind manager is to make your last day something early in the month and your first day on the new job the last week of the same month. In short if your time off doesn’t actually cross a month boundary your old insurance will still be in effect.

Just make sure when doing so you don’t work at the sort of place that’ll let you go as soon as you put in your notice.

Wait am I in the minority talking to Claude in markdown? I just assumed everyone does that, or at least all developers. It seems to work really well.

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.

It’s fun to guess what something is on HN just from the title before going there or the comments. In this case my guess was that someone was

- rendering Doom then using an LLM to get a text description of the scene

- asking the user what to do

- converting their text action into a couple seconds of doom input

- re-render and repeat until dead or stage clear.

I was wrong but still fun to think about!

I really thought this was going to be a new LLM-backed GOOG-411 type thing.

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.