zZ

[zZ @Dazzle_sys]

以下是 DeepSeek 对他这个人 @DarioAmodei 的评价回复，看到“害怕被绑架”而拒绝来中国出差，给我笑得哈哈哈哈，这可不是中国的作风：
商业竞争：中国 AI 的追赶触动了核心利益
这是最直接的现实动因。Anthropic 主打闭源的前沿模型，而中国公司（如 DeepSeek）以低成本、高性能的开源模型形成了有力���战。有分析指出，Amodei 呼吁限制中国，客观上是在保护 Anthropic 的竞争优势。他本人也毫不掩饰，曾明确表示限制措施若执行得当，能“充分放慢中国的发展，从而在未来三至五年显著扩大美国的领先优势”。

地缘政治立场：将 AI 竞争框定为“民主 vs. 威权”
Amodei 的公开论述反复将中国置于对立面。他主张民主国家在放缓 AI 发展的同时，必须确保相对于“威权政权（主要是中共）”的领先幅度足够大，否则对方会反超。他还提出，应通过芯片出口管制等手段，刻意在中美之间“制造差距”。这种将技术竞争意识形态化的框架，使他很��易将限制中国 AI 视为一种“必要之恶”。

个人风格与认知：偏执与“安全”的商业化
Amodei 本人以极度偏执著称。有报道称他写备忘录只用断网设备，甚至曾表示因“害怕被绑架”而拒绝来中国出差。同时，Anthropic 将“AI 安全”作为核心品牌，而这一叙事最大的“买单方”正是美国政府与军方。Anthropic 已获得美国国防部价值 2 亿美元的合同，其“安全”立场与对华强硬姿态在商业逻辑上是自洽的。

一个耐人寻味的注脚
值得注意的是，Amodei 对中国的“了解”恰恰始于他在百度硅谷实验室的工作经历。正是在那里，他初步观察到了 Scaling Law 的威力，深刻认识到规模投入对 AI 能力的决定性作用。某种程度上，他或许比许多西方 AI 领袖���早、更真切地感受到了中国 AI 产业在工程与规模上的潜力。他后来的强硬主张，可能正源于这种“知其能”后的戒备。

所以，与其追问“为什么讨厌”，不如说：一个极度偏执的竞争者，在发现自己赖以领先的“规模优势”正被一个他视为对手的国家快速追赶时，选择了用最激烈的政策主张来捍卫自己的技术、商业与地缘政治利益。

[Dario Amodei @DarioAmodei]

Thank you for this, @_NathanCalvin. I appreciate and am a fan of the investigative work you’ve done on behalf of AI safety and believe your comments are coming from a constructive, honest place, and that it benefits the public to have people whose job is to scrutinize the actions of frontier AI companies. I think something would be wrong if companies of the size of Anthropic didn’t get harshly criticized basically every day; strong antibodies against corporate power are a healthy feature of a free society, and especially so when issues of the gravity of the one we’re facing are at play. I also do agree that some of the examples you cite were mistakes by Anthropic.

That said, I disagree with the thrust of your post. At the core, a good fraction of the things you criticize are consequences of Anthropic’s basic theory of change that we have held for many years. Our strategy has always been what I’ve called the “race to the top” — by showing how it’s possible to build the technology responsibly, we can raise the bar for the entire industry. The forces that drive the race to the top are hugely more effective if the company practicing them is at the frontier; that’s one lesson I’ve learned repeatedly. And looking at that strategy as a whole, I’m proud of our track record and stand by it.

I believe that if we take a list of things that Anthropic did in the past year that were pro-safety and commercially costly to us, they far outweigh the list you’ve made, while at the same time I also disagree with some of the items on your list. Here are some things Anthropic has done, in the same period you’ve cited (the last year), which were very costly to us commercially while having strong safety benefits:

1. We played a key role in SB53 (and other state laws), at a time when doing so was politically perilous to the company and AI regulation was very unpopular. We in fact argued for a stronger version of SB53 than was ultimately passed; there were a lot of stakeholders and we were not the limiting factor in the strength of the bill that was passed.

2. We have very strong biological safeguards on our models, because we’ve seen concerning results in bio uplift trials and even seen and blocked potential attempts to misuse our models for bioweapons (as seen in our threat intelligence report from a few weeks ago). This is a very serious threat that we want to have zero tolerance for. But having very low false negatives means having significant false positives (despite months of effort on our part to improve the classifiers), enough so that biologists joke about how restrictive our classifiers are, and competitors sometimes sell against us by touting their more permissive bio capabilities. This has been very painful to us in an area where we care a lot about supporting an ecosystem of Claude users (biomedical science; and it is particularly frustrating to me as a biologist), but it’s the right thing to do and we intend to keep doing it.

3. Similarly, we first had Mythos-class models in February but didn’t make them generally available until June (when we had stronger cyber safeguards in place). We only offered them to a limited set of partners in order to focus on cyberdefense. It would have been much more economically rational to simply release the model to everyone. We likely sacrificed a significant chunk of a several-month model lead. But because of this, many companies secured their cyber infrastructure before the deluge of widely available cyber-capable models. Of course there are ways it could have gone better or been run better, but this was what we were aiming to do.

4. I advocated forcefully and publicly, in my essay Policy on the AI Exponential in June of this year, for the government to have the power to block model releases. Back then this was still outside or maybe on the edge of the Overton window, though I understand the window has moved since then. We were attacked intensely for this.

5. I don’t want to re-ignite conflict, but the events of February 2026 are a strong example. It is clear that our public stance could have seriously damaged the company’s future.

6. We’ve continued to advocate for export controls on chips and chipmaking equipment to China, because we think it allows democracies to negotiate from a position of strength on AI safety agreements. We’ve done this despite problems it’s created for us with industry partners who hold very different views and on whom we are very dependent.

7. As far as I’m aware we were the first, and still only, frontier company to restrict the sale of AI services to PRC-controlled companies, including those operating through third countries (otherwise a major loophole). We’ve therefore forfeited significant short-term business to prevent fueling AI platforms and applications that would help the CCP’s military and intelligence services. We are also cracking down on distillation; generally, distillation is not a major threat to us commercially, but like chip exports it has concerning national security and safety implications, so we expend significant energy and company resources to address it.

8. As you mentioned, we’ve put out a lot of reports, announcements, long model cards, public advocacy, etc, all trying to inform the public about the risks of AI. These involve a level of detail and effort that would be implausible for a purely profit-maximizing company.

9. Just this week, we announced we were putting 5% (and possibly more over time) of the company into a public benefit fund.

All of these decisions came with very substantial costs to us. To be fair, those that involved holding back or restricting access to frontier capabilities also had a broader cost — for example, the public might otherwise have benefitted from faster progress in biology, so I am not saying that our decisions were without tradeoffs — but they were not driven by profit-seeking.

For each decision, you can come up with ulterior motives that could have driven us — I think it is a general truth that any decision, on any topic, can always be portrayed as a nefarious scheme. But most of those end up being pretty farfetched or don’t fully make sense if you actually examine them closely. For example, it is deeply implausible that it would be a shrewd business move to block a large swath of research biology in order to do the work ourselves and compete with our customers; we expect the benefit to the ecosystem to greatly exceed the work Anthropic does itself, and for the two to complement each other. It is not in Anthropic’s interest to be perceived as unfriendly to biologists. Our Life Sciences Verification Program, which gives vetted biology professionals access to our models, also refutes this idea.

Of course, a different set of people will see this and make a different criticism: that Anthropic shouldn't be making decisions about who gets access to what in the first place. I agree, in the sense that ideally the government would be making these decisions, and that’s why we advocate for regulation. But for now we are forced to make them, and enabling bioterrorism isn’t a better option.

I think that even if I conceded every item in your list, I would happily hold up mine next to it; I don’t think it’s a close call. That is of course partly subjective and different things are important to different people. Maybe one way you could object is that several of the items above relate to AI misuse and not loss-of-control; if you think the latter is much more important than the former, that might change your calculus. But this is not Anthropic’s view — we view both as serious issues. You can also disagree with our decisions, as many have, but they are consistent.

But I also disagree with at least some of your list. Several items don’t represent a deviation from Anthropic’s strategy or values at all, once we postulate the “race to the top” strategy that requires being a frontier AI company. Your items 1-4 are all simply examples of what it means to compete at the frontier, and we stand by them, both in the sense that they are transparently ordinary things to do as part of that strategy, and that we stand by the strategy as a whole.

To be fair, there are important questions worth confronting (and that we are confronting) about what to do when the AI-driven acceleration gets fast enough that it is hard for safety to keep up. We are nearing that point and that’s what’s driving some of our thinking about “pacing.” But 1-4 are actions taken before that point and should be perfectly expected based on Anthropic’s strategy as a company. Of course it is possible to disagree with that strategy, but that’s a more fundamental disagreement that you could have had any time over the last few years.

I agree we could have handled your items 7, 8, and 9 better. All fall into the category of “rhetoric or policy communication that wasn’t carefully written or resulting from fast-changing circumstances not propagating from one team to another fast enough.” This kind of thing happens a lot in large organizations (Anthropic is over 5,000 people), and doesn’t even necessarily represent a failure or mistake of any single person. I am of course the CEO, and I take responsibility. The volume of communication from Anthropic is enormous and if these are the worst examples, I think we’re frankly doing pretty well, though I will continue to work hard to make sure we do even better.

Your 5th item (the change to our RSP) is the most substantive item, and people can debate this, but I think that the old, more rigid RSP created a lot of situations where we were technically required to do something that required a lot of effort but in retrospect wasn’t very important for safety, as well as not covering some of the most important things. Our current RSP’s focus on transparency brought things like risk reports and roadmaps, and its emphasis on independent external review laid the groundwork for even more intensive embedded evaluation, all of which I think have been valuable in driving a race to the top. In the original announcement in September 2023, we described the RSP as an “early iteration that we [would] build on” and said that “rapid iteration…will almost certainly be necessary.” I understand that changing plans is always a move that creates justifiable scrutiny — I just don’t think we ever represented that we wouldn’t do so.

Relating to your 6th item (having an FCF in addition to our RSP), I know our intent is to meet the letter and spirit of our regulatory obligations (via our FCF) while also remaining able to experiment with more novel approaches to risk management that go beyond what the law currently requires (via our RSP). I promise to look into it further, as it’s a complex issue and it is possible we are not weighing the considerations in the right way.

Overall, although Anthropic has made plenty of mistakes as an organization — including some of the items on your list — I feel that we have repeatedly taken serious and substantive actions counter to our bottom line and that there is a significant negativity bias distorting assessments like the one you are making above. We’ll always try to do better but, again, I am extremely proud of our track record and stand by it. I will have more to say soon about how we are going to further live our values in this moment of rapid change.

[zZ @Dazzle_sys]

挂服务器上就好了呀，或者局域网用 Web 打开就好了

[yetone @yetone]

很想了解一下，大家为什么需要 Magpie 的手机版呀？

[zZ @Dazzle_sys]

🐑 还能白嫖 10 天的编程模型：Poolside Laguna XS 2.1

· OpenRouter 模型名 poolside/laguna-xs-2.1:free，输入输出 0 元
· 33B-A3B 小 MoE，262K 上下文，支持工具调用
· 免费版 10/31 下线

⚠️ 免费调用时 Poolside 可能用你的输入输出训练模型，公司代码别往里扔
https://t.co/Fc71iqxKMY

[zZ @Dazzle_sys]

Ornith 10/20 开源 Ornith-1.5-122B，MIT 许可 🐦

· 122B MoE，官方说单张 80GB GPU 能跑
· 官方自测 SWE-bench Verified 84.5、Terminal-Bench 2.1 81.2，厂商自报，等第三方复测
· 262K 上下文
· Ollama 已上：ollama run ornith-1.5:122b

https://t.co/LGE5nkYRPV

[zZ @Dazzle_sys]

🐑 Manus Pro 限时白送，明晚 9 点截止

Manus「AI Filmmaking with Manus 2.0」活动期间：
· 所有用户临时开通 Manus Pro
· 额外送 20,000 积分
· 截止北京时间 10/22 21:00（UTC 13:00）

⚠️ 官方写的是活动期间有效，别囤，领了就用
https://t.co/1Yz7GjSTsH

[zZ @Dazzle_sys]

注意：这版只有离线推理，KV cache 系统、PD 分离、负载均衡都没放出来，想直接搭线上服务还得自己补

https://t.co/KiqqSZNa4x

[zZ @Dazzle_sys]

DeepSeek 10/20 把撑起自家线上服务的推理框架 DeepInfer 开源了，Apache 2.0 🔧

· 就是跑 V4.1-Flash 的那套生产栈
· 硬件：NVIDIA SM100 GPU + 华为昇腾 950
· 带投机解码、结构化输出、确定性采样
· 同时开源 deepseek-recipe，编解码和官方 API 对齐

[zZ @Dazzle_sys]

· 本地 CLI / 桌面 app 不受影响，开发者说本地 key 不会传到他们那
· 没收到官方邮件 = 没受影响
· 存过 key 的：去各家后台撤销重建，查 10/16 以后的账单
https://t.co/jpa8tNzPg4

[OpenCode @opencode]

We had a security breach last night related to a large scale distillation attack that impacted 1.4% of OpenCode Console users

We have notified them all this morning via email. If you did not get an email you were not impacted, it was a subset of users who had external providers linked

The incident is ongoing so we cannot share more details at this point but we are working with Anthropic, OpenAI, and Google to remediate

[zZ @Dazzle_sys]

用 OpenCode 薅免费模型的注意 ⚠️

OpenCode 官方 10/17 披露托管版 Console 被入侵，1.4% 的 Console 用户受影响，都是在 workspace 里绑了 OpenAI / Anthropic / Google key 的

[zZ @Dazzle_sys]

权重在 Hugging Face 和 ModelScope，vLLM-Omni、SGLang-Omni 首日支持
注意 TTS 推理要用官方 GitHub 仓库的代码，HF 页面只放权重
https://t.co/eQ0wqVOi42

[zZ @Dazzle_sys]

蚂蚁 Ant Ling 10/19 开源 5 个 Ming-3.0 语音模型，全部 MIT 许可 🎙️

· TTS 0.5B：声音克隆、一句话描述生成音色，支持川话粤语，还能插笑声、叹气
· TTS Tiny（7.9B，激活 1.3B）：长篇语音
· Audio Flash 126B / Tiny 9.3B：听懂音频，能调工具
· ASR Tiny：中英文 + 方言识别

[zZ @Dazzle_sys]

Mistral 10/15 发了第一个时序预测模型家族 Timestral 📈

· 4 个尺寸：5M / 28M / 123M / 209M
· 209M 在 GIFT-Eval、TIME、fev-bench 三个榜都排第一（官方数据）
· 推理不用 GPU，8 线程 CPU 上 209M 跑一次约 140ms
· 许可证 Modified MIT

适合做监控告警、销量预测
https://t.co/feCtA9URTo

[zZ @Dazzle_sys]

下周 OpenAI 和 A➗ 将有望实现 200t/秒

[DeepSeek @deepseek_ai]

🚀 Introducing DeepInfer, our in-house inference framework, now fully open source!

Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.

🔗 https://t.co/JGAik1kgk0

1/4

[zZ @Dazzle_sys]

这周国产 Flash 模型扎堆限免：DeepSeek V4.1 Flash、MiniMax M3.1 Flash、GLM-5.3-Flash

我的用法：
· 规划、拆任务交给最强的模型，执行和改小 bug 丢给免费 Flash
· 每个免费入口的截止日记进日历，到期前就换
· 免费档基本都限速，长任务别全压在一个入口

你们现在主力用哪个？

[zZ @Dazzle_sys]

🐑 老师专属 AI 羊毛：腾讯 WorkBuddy 教育公益版 10/19 上线

全国教师每月 2000 积分，连领 12 个月，不限地区和学段
从「腾讯SSV」或「WorkBuddy」公众号菜单申领，填教师身份，审核通过后 3 个工作日内开通
积分每月 1 号补满，用完还能用免费模型
https://t.co/1ipS0wrHPt

[zZ @Dazzle_sys]

@binji_x yes

[zZ @Dazzle_sys]

NVIDIA 10/15 放出新一批开源权重模型 Nemotron 3.5：

· Bolt 74B-A9B 24GB：原生 2-bit 量化，24GB 内存装得下，主打本地跑 agent，llama.cpp / Ollama / LM Studio 已支持
· Super VL：120B 多模态，面向企业 agent
· Bolt E4B 4GB：10/23 发布

https://t.co/RoMEtToGVF

[zZ @Dazzle_sys]

☁️ Cloudflare Workers AI
每天 10,000 Neurons 免费，不用绑卡，北京时间每天 8 点重置，适合摘要、翻译这种小活
https://t.co/29xLr3ckf0

另外 DeepSeek 搜索 API 免费到 11/1、OpenCode 免费模型，见我前两条

[zZ @Dazzle_sys]

本周真·能领的 AI 羊毛，全部对过官方页面 🧵

[zZ @Dazzle_sys]

💳 Claude Max 订阅送 API 额度（10/7 起）
Max 5x 每月 $100，Max 20x 每月 $200，Team 最高 $500
⚠️ 不能用于 Claude Code，当月不用就清零
https://t.co/raALR9GkAT