thx67

The lede is that Thomson Reuters is selling voter data to ICE.

The callout is real, and I am glad you pushed back.

You might have unaddressed writing trauma. Just because the abstract isn't dogshit, doesn't mean it isn't templatized mad sloplibs. The numbers, the framing, the wins; itz giving people hives.

No outs, we need a controlled vocabulary.

Firefox ESR in Debian12, won't start. 140.12.0esr (64-bit)

This is the entirety of the log message.

    [chrome-demo] chrome assets + engine wasm ready
    [gecko] warning: unsupported syscall: __syscall_madvise
    [gecko] embed-xul: main() on the app pthread (PROXY_TO_PTHREAD)
    [gecko] embed-xul: GECKO_GL_PASSTHROUGH=0
    [gecko] embed-xul: GECKO_COARSE_CLOCK=1
    [gecko] [libxul] abort: Assertion failed
    [gecko] Aborted(Assertion failed)

How about if it doesn't say RENT, it means you own it and first sale doctrine applies. We can't let them weasel and wordsmith their way out of things.

I too came from vinyl hiss and found only cold hard silence.

I had it refuse OpenCL code that had nothing to do with anything, just slightly advanced cellular automata.

Claude now hijacks your mouse in tmux!

Yeah, I use tmux all day. Technically great, but the UX for tmux smux.

You end up having to wrap everything in adapter scripts because it is so verbose as well as so unmemorable, along with that, it has no built in help, not even -h

Thank you for supplying all that info. I wasn't implying your app did anything nefarious, just the kinds of things I would like to see on an App Nutrition label. That is cool that you wrote it in .NET, any hiccups with that stack? I did some MacDev using Lua like 15 years ago, it worked out pretty well.

Could be pulling in webkit. 9MB doesn't mean it isn't based on a web renderer. It would be awesome if software came with a nutrition label. IO, does it phone home, memory safety, does it use web renderer, memory usage, etc.

> Since monitoring task performance and adapting behavior accordingly are central to metacognition, we posit that mod- els capable of accurately judging their own performance are better positioned to improve it. We operationalize this idea via two novel mechanisms: reinforcement learning with metacognitive feedback (RLMF), a paradigm to refine completion rankings during preference optimization based on the quality of a model’s self- judgments of performance, and metacognitive data selection, which uses similar self-judgments to identify high-value training examples, outperforming naive active learning.