You should be continually contributing over time allowing you to benefit from the dips by buying low. This offsets the losses and is called dollar cost averaging,

You should name it Hawk, so people can call it Hawk-Tui.

So is Visual Studio basically dead at this point? Are any new programmers choosing to use it?

I run Emby and MythTV on a Beelink Mini PC. It is a little pricey compared to some of the options you mentioned but not by too much. It works really well and is very quiet:

https://www.amazon.com/Beelink-SER5-5560U-500GB-Computer/dp/B0B3WYVB2D

I remember when SFC was first introduced, I excitedly wrote a script to invoke it remotely so I could use it on a user’s pc when they called to fix their problem. To this day I have never run that script. This was in 1998.

What are your use cases?

Librewolf is great. Secure and private by default. For compatibility it is nearly as good as Firefox.

A lot of good stuff here. The three things that are most notable for me are:

Notepadqq

Fsearch

Librewolf

Allowing cookies for websites you are logged into makes sense. If you are going to login the site already knows who you are can track you, so you do not lose much with the exception. What I do for some sites like google services is access them from a separate browser.

Good question! After installing Emulators on my Steamdeck I realized it could run as a desktop. Also, I learned it was a rolling release. This seemed attractive to me, so I wanted to hear how mainstream this could be.

Sounds like the answer is not very. Some other good suggestions in this thread I might try, though.

Not anymore according to Wikipedia:

SteamOS, version 3.0. This new version is based upon Arch Linux with the KDE Plasma 5 desktop environment

deleted by creator

Does it support offline access?

Does the person she is speaking with know where she is?

Loud grunting and farting noises intensify

Have a look at this paper from MS research -> https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/

“ Recent research has focused on enhancing the capability of smaller models through imitation learning, drawing on the outputs generated by large foundation models (LFMs). A number of issues impact the quality of these models, ranging from limited imitation signals from shallow LFM outputs; small scale homogeneous training data; and most notably a lack of rigorous evaluation resulting in overestimating the small model’s capability as they tend to learn to imitate the style, but not the reasoning process of LFMs. To address these challenges, we develop Orca, a 13-billion parameter model that learns to imitate the reasoning process of LFMs. Orca learns from rich signals from GPT 4 including explanation traces; step-by-step thought processes; and other complex instructions, guided by teacher assistance from ChatGPT. To promote this progressive learning, we tap into large-scale and diverse imitation data with judicious sampling and selection. Orca surpasses conventional state-of-the-art instruction-tuned models such as Vicuna-13B by more than 100% in complex zero-shot reasoning benchmarks like Big-Bench Hard (BBH) and 42% on AGIEval. Moreover, Orca reaches parity with ChatGPT on the BBH benchmark and shows competitive performance (4 pts gap with optimized system message) in professional and academic examinations like the SAT, LSAT, GRE, and GMAT, both in zero-shot settings without CoT; while trailing behind GPT–4. Our research indicates that learning from step-by-step explanations, whether these are generated by humans or more advanced AI models, is a promising direction to improve model capabilities and skills.”