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ENCHIN A wild and wacky mission: Delivering smiles to ENGENE! #ENHYPEN# #엔하이픈# #ENCHIN# #엔친# #WONCHU# #원츄# #NoxStar# #녹스타# #JAKEY# #제이키# #SnoWe# #스노위# #KISHU# #키슈# #PU_NI# #퓨니#

【ご報告】 6/1日からSNOW entertainmentさんの所属タレントとして活動することになりました✨マルチな活動精一杯頑張りますっ🫶🏻 SNOW entertainment株式会社 #北海道# #芸能事務所# #北海道芸能事務所# #snowentertainment#

公司的 SQL 仔和数据分析师要被这个开源神器干失业了！老板直接用自然语言提问，AI 就能自动打通底层数据库生成数据报表，关键是还不会胡乱捏造指标。 这玩意真有人做出来了——GitHub 爆火开源项目 WrenAI，目前已经斩获 16.8k Star，采用 Apache 2.0 协议开源。它是一个专门给 AI Agent 打造的 Generative BI（生成式商业智能）引擎。简单来说，它在你的数据库和 AI 之间塞了一层“业务语义理解”，不管是 PostgreSQL、ClickHouse、Snowflake 还是 BigQuery，连上就能直接让人话变成准到离谱的 SQL 和可视化图表。 🔥 核心卖点： • 通吃 20+ 种主流数据库：大小数据库一键打通，不挑食 • 独家业务上下文层：AI 不再瞎猜表结构，查询零幻觉 • 自动出可视化图表：说句人话直接生成漂亮仪表盘 • 无缝对接 AI Agent：跟 Claude Code、Cursor 秒无缝集成 • 完全开源可私有化：敏感数据不外流，本地部署超省心 🔗 传送门：

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.

#Polymarket’s# monthly user retention outperforms more than 85 percent of 275 sampled crypto projects, including DeFi protocols, wallets, and exchanges. While many platforms lose users after the first month, its cohorts show meaningfully higher rates of return trading.— Large early trades above the 90th percentile and at least $100 achieve only a 52.3 percent hit rate on final outcomes. That is a statistically detectable but economically modest edge over the roughly 50 percent baseline of all resolved trades. In the 2024 presidential markets, about 40 percent of traders participated exclusively in Trump contracts, and 71.8 percent touched Trump YES at least once. Only 0.7 percent of participants were active across more than two candidates, revealing extreme specialization rather than broad portfolio trading. — Kyle’s lambda in the same election market fell from roughly 0.518 in the early months to about 0.01 by October. As volume expanded, the market’s vulnerability to price impact declined by more than an order of magnitude. — Trading activity in the final quarter showed a clear intraday pattern, peaking between 09:00 and 20:00 UTC. The most frequent traders displayed even sharper concentration during U.S. market hours. — Across resolved markets since 2023, overall predictive accuracy on @Polymarket sits near 73 percent and rises to roughly 84 percent in markets exceeding $100,000 in volume. Only about 3.4 percent of markets faced formal disputes, most of them triggered by ambiguous wording rather than contested facts.