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비니리우~~ 날씨 더우니까 여러분은 안 쓰셔도 돼요,, 리우는 평소에 비니를 늘 쓰고;; 잠시만요 이마에 땀 좀 닦을게요 😛 #BOYNEXTDOOR# #리우# #RIWOO# #DAILY_BOYNEXTDOOR#

여러분.. 올블랙에 신발 포인트 해보셨나요?ㅎ 저는 반대로 올컬러에 블랙 신발을 신었습니다 헿 ㅋㅋㅋㅋㅋ #BOYNEXTDOOR# #RIWOO# #리우# #DAILY_BOYNEXTDOOR#

오늘은..! 성호 칭찬하는 날;;;잏 오늘 하루 종일 제 칭찬밖에 안 보여서..칭찬 감옥에 갇힌 기분이에요.. 💕 💕 저도 우리 팬분들 칭찬할래여!! 항상 성호 사랑해 주고 생각해 주는 모습이 너무 사랑스럽구 이뻐서 더 사랑하고 보고 싶어여-!! 🫠 🫠 #BOYNEXTDOOR# #SUNGHO# #성호# #DAILY_BOYNEXTDOOR#

오늘은 성호시에 성호 생각하는 날인 거 아시죠? 저는 여러분 생각하면서 행복하게 지냈어용 🐱 2003904만큼 사랑해 ❤️ #BOYNEXTDOOR# #SUNGHO# #성호# #DAILY_BOYNEXTDOOR#

白线 WhiteLine Daily：SNDK 业绩大幅增长，但市场已从“存储涨价”转向交易合同定价、设备订单与新架构落地；Burry 则关闭部分仓位，并将 NVIDIA 与 QQQ 空头展期至 2027 年，方向未变，但风险兑现时间被进一步拉长。 阅读全文：

Someone made a Mole review and it genuinely made my day. Andy Hutchinson over at Mostly Mac ran the open-source CLI on his M2, cleared ~13GB, and after six months of daily use called it "no subscription, no bloat, no drama." That means a lot for something I started as a weekend shell script. Huge thanks, Andy. If you make Mac content and want to feature Mole, I'd love to help, reach out and I can set you up with license keys to give away to your audience.

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.