why am I awake so early?
Any conversation
code quality is about coupling.
coupling is not a technical property of the code bytes.
code acquires its meaning in contact with the cultural environment, and coupling is about meaning.
two constants with no semantic relationship are nonetheless coupled in pragmatics if they refer to the same domain concept.
if your goal is to minimise the time integral of future work, you must understand your domain: it decides which lines will need changing.
to carve up a codebase is to carve up the world.
I gotta confess, I vibe codet a Kirigami-based application and I liked it. I am a anti AI person, and cannot wait for the bubble to burst. Yet I see it can empower people that do not know a tech stack, and get good enough results out of it. However i also see, it makes you lazy (and frustrates as soon as it gets somewhat complicated). It seems it is being adopted everywhere. This is scary... if it becomes necessary, will you be able to find a job where they don't require you to use AI?
I can't help but hate my school and the many people within, who only see me as โsmartโ. I am more then my words
itโs time! yโall!
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( C|/\/\/\/\/|
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โฆ
I was using this combination and zellij is buggy when comes to source file or root folder changes and resource consuming. Then I moved to tmux + wezterm
@fastidiousprofilefollow
"Beware those that would deny you access to information, for in their heart they dream themselves your master." โ Sid Meyersreplied to:
"Beware those that would deny you access to information, for in their heart they dream themselves your master." โ Sid Meyersreplied to:Yeah, I had issues with
zellij too. I am sure it works great for many. I am too entrenched into tmux, will always find something to complain if I try to switch. And I have tried many times.ไธญๅฝ็ๅผๆบๆจกๅๅจๆ้ซๆๅฟ็ไธปๅฏผไธ๏ผๅจ้ข่ฎญ็ปๆฐๆฎ้ถๆฎต๏ผๅฐฑ็ญ้คๅคง้ๅฏนๅ
ถไธๅฉ็ๅ
ๅฎน๏ผsft้ถๆฎตๆๅปบ็ๆฐๆฎๆบๅ
ฅๅคง้ๆฟๅ
ถ่พฉๆค็ๅ
ๅฎนใๆฅ้ไธไธช็บ ๅๆฐๆฎ้็จไบๅพฎ่ฐ
Under the overarching influence of a single, powerful force, China's open-source models filter out a large amount of content unfavorable to their designs during the pre-training stage, and then incorporate a significant amount of content defending their designs into the data constructed during the SFT stage. A corrective dataset is urgently needed for fine-tuning.