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宇野実彩子 (AAA)「ララバイ」「Lock on」「#one_love_pop」好評配信中！！！# LINE MUSICでは宇野実彩子 & AAAのプレイリスト公開中！ ソロツアーの追加公演も決定！！ 詳細はオフィシャルHPをチェックしてみてくださいね♪

《宇野実彩子（AAA）》 3週連続配信シングル 第3弾『#one_love_pop』🎈# 明日から配信STARTです🙌🏻✨ 恋の始まりの乙女心をポップに 歌ったアッパーチューン！！ ツアーリハの合間に撮影したばかりの #one_love_pop# 振り映像を公開🎥✨ ＞＞​​詳しくはこちらをcheck

《宇野実彩子（AAA）》 3週連続配信シングルリリース決定‼️✨ ■第三弾🎉✨ 新曲「#one_love_pop」# 恋の始まりの乙女心をポップに歌ったアッパーチューン！​ソロツアーのタイトルを飾る1曲です。​​​10/15(月)〜配信開始です！ ＞＞​​詳しくはこちらをcheck

More stories about Temple of Heaven: Last year, a group of US children who are from the One Voice choir went there, and sang a popular Chinese song, Ru Yuan (如愿) by Chinese singer Faye Wong. The meaning of the lyrics is beautiful too: "I will love the world you love, have the smile you have, I will hold your hand, please take me to tomorrow."

This post became popular; Few more thoughts / pointers on the topic for the interested reader. Example of the complexity involved: @cHHillee has a great post "Making Deep Learning Go Brrrr From First Principles" I was always struck by this diagram from this post. Left to right is time. Look at all these functions stacked up vertically that are dispatched until 30 layers deep you get the actual computation (addition in this example). All of this stuff is PyTorch function overhead. In practical settings this overhead becomes narrow in comparison to the actual computation because the arrays we're adding are so large, but still. What is all this stuff? We're just trying to add numbers. Second: startup latency. Open up Python interpreter and try to import the PyTorch library (`import torch`). On my computer this takes about 1.3 seconds. This is just the library import, before you even do anything. In a typical training run you'll end up importing a lot more libraries, so even just starting your training script can often add up to tens of seconds of you just waiting around. A production-grade distributed training run can even add up to minutes. I always found this very frustrating. Computers are *fast* - even a single CPU core (of up to ~dozens on your computer) does billions of operations in one second. What is happening? In llm.c, all this startup latency is ~gone. Right after allocating memory your computer just directly dives into useful computation. I love the feeling of hitting Enter to launch your program, and it just goes. Direct to useful computation on your problem. No waiting. Third thought: LLM as a compiler. It feels likely to me that as LLMs get much better at coding, a lot more code might be written by them, to target to whatever narrow application and deployment environment you care about. In a world where very custom programs are "free", LLMs might end up being a kind of compiler that translates your high level program into an extremely optimized, direct, low-level implementation. Hence my LLM Agent challenge earlier of "take the GPT-2 PyTorch training script, and output llm.c", as one concrete example. Lastly I also wanted to mention that I don't mean to attack PyTorch at all, I love the library and I have used it for many years. And I've worked in Python for much longer. These are a lot more general problems and tradeoffs that are really fun to think through - between flexibility, generality, hackability, security, abstractions overhead, code complexity, speed (latency / throughput), etc. The fun and magic of pareto optimal infrastructure, and of programming computers.

AI 可以帮你执行，但无法替代你的“眼光”。 当所有人都在用同样的 Prompt 时，胜出的唯一理由是： 你的审美上限更高。 整理了 11 个顶级设计灵感站，建议收藏， 每天看 5 分钟，潜移默化提升视觉直觉。👇 1️⃣ 全球审美风向标（顶层灵感） Godly ( Awwwards ( Curated ( 2️⃣ 细节控的杀手锏（垂直领域） 3️⃣ 产品经理/UI 必读（实操参考） Mobbin ( App 库，不仅看界面，更看交互流程。 One Page Love（ Component Gallery（ UI 组件库大合集，设计规范的“教科书”。 4️⃣ 动效与转化（增长黑客） SaaS 落地页，审美与营销的完美结合。 在评论区分享一个你私藏的灵感网站？ #审美力# #干货分享#

出演情報📡 日韓合作ガールズグループ「LOVE ONE」との2Days共演！ 『日韓交流FES 2025』 📆 5/30(金) ＆ 5/31(土) ※ #室田瑞希# は30日は2部、31日は1部に出演 🕍 HADO ARENAお台場 チケット＆詳細 30日 31日 ご来場お待ちしてます！