kekebo

Clouds of intracellular water more efficiently retained in muscle due to creatine supplementation[?]. Hydration is controlled for in some studies but not all.

I keep having had the best time with Andrej Karpathy's Youtube intros into LLM math. But I haven't compared scope or quality to this submission

Thank you for the comment. https://github.com/umlx5h/zsh-manpage-completion-generator appears to adapt this to ZSH. Have yet to try through

:)) Apparently. I don't have a better guess. Well spotted

I apologize for shortness but <3

Thank you

I just switched away from it again after it got delisted from nixpkgs due to lack of development[0][1]

[0] https://search.nixos.org/packages?channel=25.05&from=0&size=...

[1] Should be this one https://github.com/NixOS/nixpkgs/pull/351205

w/ signed counts

Overton window calculation usually starts at a minimum of two inputs without proximity requirements (except technical requirements like linguistics/ functional semantics)*

[ * Only because an opinion may appear too far removed from a given perceived spectrum-threshold for 'reasonable reasoning'.. should not necessitate collapsing the contrasting input to some purely sarcastic/humorous telos, especially when this stochastically undermines one's own chances for being afforded the inversely congruent gesture]

Is this intentional devils advocacy for the sake of balancing an expected narrative? Outside of the rarely normative definition threshold as to what constitutes spyware or not, on what data / references (if any) do you base your impression on?

And how does a perception of company trustworthiness correlate with telemetry ethics that don't infringe in some way on 'basic digital human rights' (as defined by GDPR et al, say)?

Such confidence ratings over unavailable/lost/missing data appear like a luxury to where I'm from (science wise). Does historical analysis come with heuristic privileges over other fields in that regard?

While partly debated, the first part sounds like a prime candidate for Dunning Kruger effect in (historic) action

Nice, thanks for sharing!

In this case both options seem to apply at the same time

(Corresponding wiki entry for the remote version: https://en.wikipedia.org/wiki/Telephone_card#Remote_memory_s... )

Oh boy, this takes me back. This is from memory, so take it with a grain of salt: The calling cards I mainly used as a kid in the 90s were ones with a sim-like chip on it that you would insert into a phone booth until it ran out at which point you'd throw them away. Earlier/alternative ones worked, iirc, by calling a free phone line which prompted you to enter a valid calling card number which had credit assigned to it, and then you could make your call. If there was a way to reverse engineer the algorithm for valid calling card numbers (similar to a keygen) I guess you could hack it for free calls. If someone has a more plastic / accurate memory of this please correct me

Pretty much

A common trajectory seems to be your sentiment, followed by a couple of reluctant retries, followed by eventual, disproportional reverence. I'm no different

The people who are likely have a limited capacity to.

https://x.com/afterveil/status/1746168116546093291?s=46

You may be seeing a feature as a bug

I can't speak to the differences of Openrouter to Together but the Openrouter endpoint should work as a drop-in replacement for OpenAI api calls after replacing the endpoint url and the value of $OPENAI_API_KEY. The model names may differ to other apis but everything else should work the same.

Just watch your phases a bit:

"Several studies of PSG-measured sleep report increased SWS [25, 75], decreased REM sleep [74, 75, 160], and decreased REM density (e.g., number of eye movements during REM sleep) [74, 75]. However, this pattern is not always replicated [150]."[0]

(SWS =slow wave sleep)

[0] https://link.springer.com/article/10.1186/s13722-016-0056-7

Out of interest, what are the issues you're facing? I'm having a great time with NixOS / Plasma 5

I'm guessing the assumption is that it was manipulated by an intelligent actor into the described 'perfection'.

It gets soggy and disintegrates when not consumed swiftly?

Feeding the output of a transcription service (e.g. https://youtubetranscript.com/?v=G4hL5Om4IJ4 ) into an llm for specific requests should be possible, likely in multiple steps to handle context limitations

Is there any generalizable measure of how any of these models (or their client implementation) handle code(base) context that's sent along each editing request? For my use cases this seems to be as crucial a measure as the general coding responses per file / selection / request and where implementations like Cody[0], Cursor.sh[1] or aider.chat[2] stand out

[0] https://sourcegraph.com/docs/cody/core-concepts/context

[1] https://docs.cursor.sh/features/codebase-indexing

[2] https://aider.chat/docs/repomap.html

It may be written in Javascript.

That was wholesome, thanks for linking