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เสิร์ฟแบบนิวเจน ตัวลูกเบบี้ #ผ้ามี่โปรดักชัน# #Howtoถ่ายคอนเท้น# #ม่อนแอบถ่าย# #ด้วงหมุนๆ# #BabyBabyBabyTH# #KwanCGM48# #LinglingCGM48# #LookkedCGM48# #RunmaCGM48# #CGM48# #ซีเจมของแทร่#

学 Claude Code 入门，官方文档真够用了。 如果非要补一个，我只推 claude-howto： GitHub 39.7k star、Trending 第一、跟着每个版本更新到 v2.1.206。 它把 10 个模块排成 11 到 13 小时的路线，正好一个周末，而且全是能直接抄进项目的模板，不是干读文档。 官方讲清了"是什么"，它补上了"怎么实际综合使用"。

3/5が待ち遠しすぎる…… 小野坂っちにガチでメロついてるシーンも見所なんだけど、それと同時にかんな先生とゆかり助手のHOWtoシーンでみんなの素が垣間見えるシーンも大好きなのです。 ご予約はツリーから👇

#スターズHOWTO# 見てくれた皆さん有難うございました😝 もっと日本でも活躍できるようにこれからもインドネシアで頑張ります🇮🇩 Terimakasih sudah nntn tv Jepang semamalam😊🙏🏻 Semoga aku bis a lebih terkenal lagi di Indonesia biar bisa masuk tv Jepang ya😝🇮🇩

はかなげななせに、オトナナセ。皆さんはどんななぁちゃんがお好きですか？ こちらは甘さ控えめ #微糖な七瀬💄# 『#わたしのこと』では様々な七瀬メイクのHOWTOを研究。七瀬顔コレクションとしても楽しめるので、男子も女子もぜひ♡# #西野七瀬1stフォトブック# #わたしのこと#

I remain convinced nobody knows how to pronounce or talk about $AAOI in real life. It’s one letter too long to say $TSM or $AMD. And nobody ever says to a friend“Wow, I like applied optoelectronics!”

Two weeks ago, I resigned from OpenAI to join Conduit as a founding researcher, where we're training models to non-invasively read the human mind. I've written some thoughts about what telepathy could look like by 2035 and how to get there:

The future of AI will be built by you. 🫵 Introducing #BuildWithYou# Chapter One: Finance Prompts Share the finance prompts you use to learn, research, invest, and trade. 20 standout prompts will split a 2,000 USDC prize pool. How to join: 👉 Follow @Binance and repost this 👉 Reply or quote repost with your prompt 👉 Complete the survey → Entries close: 15 Aug 2026, 23:59 UTC. Join us, and let's build the future together. 🫡 P.S. Finally, a good reason to check your ChatGPT history. Disclaimer: This is not an offer or solicitation to trade any financial product. Not available to users in jurisdictions including: US, UK, EEA, Hong Kong, Singapore, and the jurisdictions on Binance's prohibited list (see for details)

some of the HOTD fandom are genuinely exhausting. demanding 100% book accuracy knowing characters with 0 characterisation go months without any mention in F&B. no thought as to how to cram it into 8 hours, on budget while telling a cohesive, well paced story lol

Okay, the @VulcanBench results for Qwen3.8-Max are in, and it is not what I expected. First, for anyone new to VulcanBench, here's a quick TL;DR on the eval suite: 23 frontier-hard software engineering tasks taken from real merged OSS PRs, run in a Docker sandbox, 3 runs per task across all three of its effort levels. No puzzles, no random abstract stuff, all real things engineering teams would do with these models. It looks like Qwen3.8-Max has a major overthinking problem, it uses a LOT of tokens and is very slow, period, no other way to see it. My cost to run this benchmark was $126.25, to run the exact same eval suite with DeepSeek V4-Flash was only $13.60. This makes Qwen3.8-Max an insanely expensive model. The tasks Qwen genuinely can't solve fail at every effort level, extra reasoning didn't help. The regression is almost all in work it already handles: six tasks that low solves every single time account for 83% of the 26-point drop, three of them collapsing to zero. It's not losing the hard problems. It's losing the ones it already knows how to do. Since Qwen3.8-Max hit a lot of wall clock budget caps, I thought I'd share more about this. - VulcanBench caps both steps (50–200) and wall clock (5–60 min), each scaled by repo size. - This is aligned with how comparable harnesses bound agents, DeepSWE caps rollouts at 100 environment steps, sitting right inside my step range; Terminal-Bench enforces a per-task wall clock; SWE-bench Verified scaffolds typically allow 20–60 min per instance with 250–350 step limits. - Every model on my chart gets the identical budget, and Qwen is the slowest model I've tested at 20–25 min/task. Soooo... Alibaba positions Qwen3.8-Max as trailing only Claude Fable 5. But on the kind of real coding work engineering teams would actually throw at it, under a fixed budget, its best setting lands mid-pack and its default lands last, so common. If you want to optimize for accuracy, Grok 4.5 is the move. If you want accuracy per dollar, DeepSeek V4-Flash is hard to beat, heck it's 10× cheaper than Qwen and you get higher accuracy. Qwen just isn't in the game at this point, this is not a model I could see engineering teams using for daily coding work.