与「35」相关的搜索结果

做空一个山寨可能会被针对或者黑天鹅，但是按照市值从高到低做空几十上百个就是稳赢，即使某个插针或者拉几倍，整个仓位也没什么影响。 以相同金额开的空单数量越多，单个币的非系统性风险就越低。 开一个币涨10%就亏10%，开10个其中一个涨10%就只亏1%，要是开100个，其中一个哪怕涨100%也只亏1%。 单个币的空单金额可以小一些，实际操作难度很低，都不用盯盘，几十上百个空单也不可能盯得过来，所以就不用因为某一个币暴涨暴跌影响情绪。 比如川沐在账户达到35万美金的时候，总共空了102个币，当时他的操作是10亿市值以上的每个空了2万，2亿—10亿的每个空了1万，1亿到2亿的每个空了5,000。

交易员川沐用过一个策略，据说跟收菜一样简单，非常有意思，而且他把一个1万美金的账户用了35天就做到了100多万美金，超过700万人民币。 市场上幻想小资金发家致富的人很多，他也是趁热打铁，又开了一个1万美元的新账户。 结果14天后账户又多出了200万人民币。加起来只用50天就赚了1,000万，非常强！

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So here’s how America ended up with Ilhan Omar and Learing Centers. The rebels in Somalia overthrew the Marxist dictator in January 1991 and then they started killing each other over who got to rule next. General Mohamed Farah Aideed and Ali Mahdi Mohamed both claimed the presidency, and then from November 1991 to March 1992, the two sides slaughtered an estimated 14,000 people in Mogadishu (the capital) alone. The clans united to remove Barre, but they failed to replace him with anything functional. And so, Somalia has been a failed state for 35 years and Minnesota has welfare fraud.

今天有好几个开源的小模型。 Mach-1 Additive：35B，1.7bit量化，16G Mac可以流畅运行。 模型： Shieldstral 1.0 3B：多模态审核模型，仅3B，支持文字+图片输入，支持自定义审核规则。 模型： Maple-Preview：20B总参数，1B激活参数，1.58bit量化，iPhone可运行，在M4 Mac Mini上可以达到218token/s。 模型： Pokee-Isaac 28B：这个没开源，28B模型，特色是1000万上下文。luna水平和价格。 官方介绍：

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.