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[📹] Atualização da Sihyeon no RedNote Cover da musica “A Poem Titled You” (TAEYEON - OST Hotel Del Luna) #EVERGLOW# #에버글로우# #RE_CODE# @everglow_offcl #SIHYEON# #시현#

🎉โหลๆ รู้ยังเกมเด็กดี Ark Re:Code ช่วงนี้กำลังคอลแลปกับอินฟลูชื่อดัง HongKongDoll น้าอีเว้นอยู่ถึงแค่วันที่ 21 ส.ค. ถึง 4 ก.ย. นี้ เข้าเกมArk Re:Code เล่นอีเว้น"สองวันสองคืนที่มีเจ้าอยู่" เพื่อเจอกับ HongKongDoll ในเกมได้เลยจ้าา ปล.เป็นเกมเด็กดีมีภาษาไทยด้วยน้า ดาวน์โหลดเล้ย 😘💖 Download : 🎉Ark Re:Code is currently collaborating with the famous influencer, HongKongDoll! From Aug 21st to Sep 4th, participate in the Ark Re:Code event, “With You For Two Days and Nights”, to meet HongKongDoll in the game! 😘💖 Download :

這次收到 EROLABS 邀請， Cos成裡面的商店小助理 這短裙的造型是不是很可愛呢？ 現在《星隕計畫Ark Re:Code》與知名情色女神Hong Kong Doll （玩偶姐姐）合作啦！從8/21~9/4，參加《星隕計畫Ark Re:Code》「有妳在的兩天兩夜」，就可以見到女神HongKongDoll！ #EROLABS# #EROLABS#

[gif] me trying to read tinygrad code earlier :D I think the LOC requirements (which are only a proxy for simplicity) led to too great compression. You wouldn't brag about your .min.js code being 1 LOC. Imo it would be a lot more simple if the code was given room to breathe and some comments. The optimization should be: minimize LOC subject to constraint that the code is clean. Nothing that can't be fixed, too. RE code using (aside from reading), happy to consider it and work with it as a baseline on the side of PyTorch when it reaches 1.0. I've used PyTorch for many years so it's easy to go to for a strong baseline. Btw based on some comments it's worth clarifying that llm.c repo and TinyGrad repo are very different kinds of pokemons. We both want to train LLMs fast. TinyGrad wants to be an actual compiler (think: gcc) - take high-level descriptions of arbitrary networks and compile them to run fast on different backends. llm.c is more like a direct, assembly-level program, written by hand, for a very specific, narrow program (GPT-2 training loop). Unlike your typical assembly program though, you get something low level but still readable. Compilers will struggle to produce this, even if they may match or surpass the running time. It's not usually a goal of a compiler to produce readable code. So there are two ways to generate really fast code: 1) write a better compiler 2) write a better assembly-level program At the end of the day it can be both. (2) is really fun to write and you're in complete control. And any optimizations that get done by hand can help improve and challenge (1) to emit them as a special case when appropriate. Also, (1) may find and emit optimizations that could be extremely tedious to do by hand. And of course the moment you want to do something different, you'll have a lot easier time with (1) over (2). One more radical and possibly under-appreciated thought that may turn out to be wrong but I think has a decent chance to be right. I think LLMs are going to become very good "compilers" and will be capable of directly emitting excellent assembly-level programs. Code like llm.c (and descendants) could one day be a part of a few-shot prompt, to help the LLM compile the n+1 program.

Jack Ma told Elon Musk humans will always be smarter than machines. Musk’s reaction says everything. “Computers may be very clever, but human beings are much smarter. We invented the computer. I've never seen a computer invent a human being.” Ma's argument was simple. Computers are tools. They calculate faster, remember more, but humans still hold something machines don't: experience, judgment, wisdom. Musk clearly didn't buy it. “What would be an example of something that humans are better than a computer at?” Watching this exchange now feels completely different than it did then. AI agents aren't just calculating anymore. They're writing code, analyzing documents, planning tasks, deciding what to do next on their own. METR found that the length of software tasks frontier agents can complete autonomously has historically doubled roughly every seven months, with signs of even faster progress through parts of 2024. That's where Ma's argument starts to buckle. For decades, technology automated what humans did. AI is starting to automate what we assumed required a human to think. And companies were already learning to create enormous value with almost no people before this wave even arrived. Instagram sold for $1 billion with 13 employees. WhatsApp sold for $19 billion with 55. Now picture what happens when a small team can direct hundreds of machines that never sleep, never take a salary, never need an office. The question was never just which jobs disappear. It's what happens to the money once fewer humans are needed to create it in the first place. That's the question behind 2028: The Economy Without Humans. Who gets the money when machines do the work?

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Got em. I poison my AGENTS.md (and other things like code comments) all over the place with prompt injections like this to find people who don't review their code and sling it off to another human. Catches folks all the time and then its an instant ban. As I've said, I don't care if you don't review your own code. But if you're submitting code to an OSS project and crossing a human boundary, it is simple courtesy to do some human review.

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