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you can now download my song “winter breath” on itunes, spotify and google music.🎶 The acoustic version of the song will be released on Christmas Eve. I hope you enjoy my little Christmas gift 🎁for you all. #겨울숨# #winterbreath# #sooyoung# #soolo#

My official Youtube and Twitter channels are now open. Here are the link below: My first digital single will be release on 2018.12.20 6PM (GMT+9) Don't miss Winter Breath Music Video as well! #수영# #sooyoung# #겨울숨# #winterbreath# #soolo#

🚨STEVE JOBS FUE DESPEDIDO DE APPLE Después se presentó en el MIT y soltó la masterclass de negocios más cruda y sin filtros de 60 minutos jamás grabada. Cero mentiras de relaciones públicas. Cero imagen que proteger. Solo pura y brutal honestidad del hombre que construyó Apple una vez y estaba a punto de reconstruirlo aún más grande. Deja de scrollear. Mira esto esta noche en lugar de Netflix. Guárdalo y regresa cuando lo necesites 🔖

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.

Sam Altman (CEO de OpenAI): «Ya no necesitas escribir prompts.» En solo 38 minutos explica cómo usar ChatGPT a un nivel que la mayoría de la gente ni se imagina. Es una charla que dio a estudiantes de Stanford. Un amigo me pasó la grabación anoche. Después de verla me di cuenta de que estaba aprovechando apenas el 15 % de lo que esta herramienta realmente puede hacer. Mírala completa y después lee la guía que dejo abajo sobre cómo crear un sistema que se promptea a sí mismo.

Este tipo acaba de FILTRAR la forma MÁS FÁCIL de hacer dinero en 2026: Claude Code + YouTube = 62.000 $/Mes Nada complicado. (Solo 13 minutos) Guarda esto en favoritos para no perderlo.