shing0z

[shing0z @shing0z]

【レゲエ風】ムーンライト伝説 - 美少女戦士セーラームーン OP (Reggae AI Cover) / レゲイアレンジ https://t.co/banhfswC9y @YouTubeより

[Bağımsız Taraf @Bagimsiztaraf]

Ne kadar çabalamış olabilirsin ki

[🇹🇷 Transfer Haberleri ⚒ @Transferhaber6]

😅 Evet, kameraman bu emeği karşılığında maaş artışı hak ediyor.

[Today I Learned @TodayiLearrned]

FIFA world cup 2026 opening ceremony.

[Sui @SuiNetwork]

Money should move as freely as messages.

Today, Sui delivered with gasless stablecoin transfers.

Supported stablecoins on Sui can now be sent wallet-to-wallet without gas fees or managing a separate gas token balance.

[AST SpaceMobile @AST_SpaceMobile]

AST SpaceMobile Announces Launch Date for BlueBird Satellites 8, 9, and 10

https://t.co/AqgzCO9n6q

[Science Magazine @ScienceExpand]

The microorganism has a chainsaw like mouth

[🥊 Great Chef Boxing 🥡 @GreatChefBoxing]

Naoya Inoue 🇯🇵 Vs. Floyd Mayweather Jr. 🇺🇸

[The Insider @insiderinvests]

$SoFi Can somebody smarter than me explain what happened yesterday in the pre-market at 7 am?

[Andrej Karpathy @karpathy]

Three days ago I left autoresearch tuning nanochat for ~2 days on depth=12 model. It found ~20 changes that improved the validation loss. I tested these changes yesterday and all of them were additive and transferred to larger (depth=24) models. Stacking up all of these changes, today I measured that the leaderboard's "Time to GPT-2" drops from 2.02 hours to 1.80 hours (~11% improvement), this will be the new leaderboard entry. So yes, these are real improvements and they make an actual difference. I am mildly surprised that my very first naive attempt already worked this well on top of what I thought was already a fairly manually well-tuned project.

This is a first for me because I am very used to doing the iterative optimization of neural network training manually. You come up with ideas, you implement them, you check if they work (better validation loss), you come up with new ideas based on that, you read some papers for inspiration, etc etc. This is the bread and butter of what I do daily for 2 decades. Seeing the agent do this entire workflow end-to-end and all by itself as it worked through approx. 700 changes autonomously is wild. It really looked at the sequence of results of experiments and used that to plan the next ones. It's not novel, ground-breaking "research" (yet), but all the adjustments are "real", I didn't find them manually previously, and they stack up and actually improved nanochat. Among the bigger things e.g.:

- It noticed an oversight that my parameterless QKnorm didn't have a scaler multiplier attached, so my attention was too diffuse. The agent found multipliers to sharpen it, pointing to future work.
- It found that the Value Embeddings really like regularization and I wasn't applying any (oops).
- It found that my banded attention was too conservative (i forgot to tune it).
- It found that AdamW betas were all messed up.
- It tuned the weight decay schedule.
- It tuned the network initialization.

This is on top of all the tuning I've already done over a good amount of time. The exact commit is here, from this "round 1" of autoresearch. I am going to kick off "round 2", and in parallel I am looking at how multiple agents can collaborate to unlock parallelism.
https://t.co/WAz8aIztKT

All LLM frontier labs will do this. It's the final boss battle. It's a lot more complex at scale of course - you don't just have a single train. py file to tune. But doing it is "just engineering" and it's going to work. You spin up a swarm of agents, you have them collaborate to tune smaller models, you promote the most promising ideas to increasingly larger scales, and humans (optionally) contribute on the edges.

And more generally, *any* metric you care about that is reasonably efficient to evaluate (or that has more efficient proxy metrics such as training a smaller network) can be autoresearched by an agent swarm. It's worth thinking about whether your problem falls into this bucket too.

[Elon Musk @elonmusk]

Cybercab has started production

[stock goat @kylewhitegoat]

$SOFI 🚨BREAKING NEWS🚨

SOFI USD Stablecoin $SOFID market cap just hit $88 Million 🔥

[おのだ/Onoda @onodamasashi]

本当に安いのはドバイ。行けんけど😅

リッツカールトン3万円、マリオット1万円など

[旅人スキヤキ🇯🇵🇹🇭世界一周中 @Thai__Suki]

チェンマイで見つけた
日本人が絶対好きなレストラン。

旧市街の中に突然
ジブリの世界が現れる。

遺跡にしか見えない場所で
普通に飯が食える。

カフェメニュー、タイ料理、西洋料理も
安くて充実してた。

チェンマイで一番非日常感を感じられるレストランかもしれない。

場所
↓

[Tak(今村 拓真)｜AIと海外移住 @TakMichiShirube]

トビリシにゴールドジムは、控えめにいって最高なニュースですね！

Vakeにある、コワーキングとジムが一緒になっている施設を契約していたけど、あそこも本当に良かった。

しかも、確か月15,000円しないくらいだったので、トビリシ最高！ってなった！笑

[🇹🇭バーツ両替レート情報 jpy-thb.com @THB_Best_Rate]

タイ バンコク 現地時間 2/27 14:02の両替レート
1万円=2,000バーツ
(1バーツ=5円)
@ Super Rich Thailand

[ぷくこ🇹🇭タイ情報サイトBANGKOK GIRLS NOTE運営 @pukuko15]

大好きでどうしても飲みたいコーヒーがタイにあって、でもずっと混んでるって聞いて行けてなくて、朝イチ行ってきた！！

そしたら開店前やのに並んでる😳すごすぎー！！久々に飲んだSupremeはやっぱり美味しくて。

あの冷たくて滑らかな口当たりは他では味わえない…！シナモンの風味も良いアクセント。いやー満足です☺️

8時オープンでコーヒーを堪能してお店を出た後は店頭には誰も並んでなかったから、逆に9時くらいに行くのが正解なのかもしれない。
知らんけど👀

📍FACTORY COFFEE
BTSパヤタイ駅徒歩1分

[高橋洋一（嘉悦大） @YoichiTakahashi]

ついに体育館でなく官邸に呼び出しかwww→高市早苗首相、日銀の植田和男総裁と16日夕に会談へ - 日本経済新聞 https://t.co/GEar9hvHXk

[Visit_DaNang_Vietnam @VisitDaNangVN]

Spent Valentine’s Day watching the most beautiful sunset in Da Nang 🌅💕 The sky turned pink, orange, and gold - literally painted the perfect romantic scene! Who needs expensive dates when you have THIS view? 😍✨ #DaNang #ValentinesDay #VietnamSunset

[旅行会社パーパスジャパン @purposejapan]

⚽⚽日本代表応援ツアー募集中🔥🔥
2026サッカー北中米大会 🇺🇸🇨🇦🇲🇽を現地で応援📣
アメリカ・ダラスの1試合だけ応援するものから3試合すべて追いかけるコースまで、全て安心の添乗員付き！チケット手配もご相談ください🎫

一緒に現地で最高の景色を見ませんか？