与「QUI」相关的搜索结果

📂🇫🇷FLASH - En France, la justice explore une piste inédite qui pourrait, à terme, conduire au blocage de X. D’après L’Opinion, plusieurs magistrats cherchent à remettre en question le statut d’hébergeur dont bénéficie la plateforme, pour la requalifier en coauteur des infractions commises par ses utilisateurs Une manœuvre juridique inédite qui reviendrait à tenir une plateforme pour responsable de millions de messages qu’elle n’a ni écrits ni validés. Le calendrier ne doit rien au hasard. À neuf mois de la présidentielle, cette offensive judiciaire vise moins les infractions en ligne que la ligne éditoriale d’un homme qui a osé critiquer ouvertement le pouvoir français.

I just landed in Shanghai 🇨🇳 I’ve had the best shrimp wontons ( 鲜虾馄饨) and I can’t believe how quiet the streets are. There are people everywhere but EV adoption is huge, so it’s like a giant beast moving silently. My hotel room AC goes down to 18 (real) degree and Chinese people are super kind and helpful.

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Last night Apple briefly removed Telegram from the AppStore because a user had planted illegal porn in a public chat. Telegram was restored within hours. But I want to explain what happened — to warn other app developers and help protect online communities from similar attacks. Because Telegram quickly removes illegal content from public groups using all kinds of moderation tools, the attacker had to resort to a technical trick. He inserted AI-modified illegal content by editing an old message in an active group chat. As a result the content was effectively hidden from the group’s members, preventing them from seeing/reporting it. The attacker was a takedown extortionist: someone who demands ransom from group owners in exchange for not targeting their communities. These extortionists use automated accounts to plant illegal content in public groups and then report it directly to Apple, attempting to trigger the removal of legitimate communities whose owners refused to pay them. From a practical standpoint, illegal pornographic content in Telegram’s public groups is not a systemic problem. Our moderation is effective. The fact that attackers must resort to backdated, effectively invisible content and other technical tricks proves this. ⚠️ However, there are two important lessons here for app developers and online communities: — Extortionists have found a way to manipulate Apple into overreacting. Apple removed Telegram from the App Store before contacting us. This creates a potential systemic risk for every mobile app that hosts user-generated content. If an app used by more than a billion people can be removed from the App Store without prior warning, any app can be. — The tactics used by takedown extortionists are evolving, putting communities across social platforms at risk. Telegram has extensive experience identifying the tricks used by coordinated reporting gangs and protecting legitimate communities (even when doing so risks our own app being temporarily removed from the App Store). Other platforms may not be equally prepared. Stay vigilant! ☝️

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.

The next phase of markets isn't about choosing between traditional finance and crypto. It's about bringing them together. 🤝 Binance now represents 74% of the US$116B+ TradFi ETF Perpetuals market, underscoring how quickly users are embracing crypto-native ways to access traditional financial markets.