nsob retweeted
He’s probably a China hawk for purely opportunistic and selfish reasons (he wants to ensure Anthropic’s dominance). I doubt Baidu left a mark. I wouldn’t be surprised if he even forgot about his time at Baidu. He also went to Lowell and that school is majority Chinese.
I've been asking myself the same question for a while, but I don't know if there is any lore that explains it. I've always assumed that something had happened while he was at Baidu, because while I have no doubt that he is a sincere liberal imperialist (which for whatever reason is very common in tech where fear-mongering about "the CCP" is almost a national hobby), it's hard to believe that he would have worked for a Chinese company had he already been such a China hawk at the time, but I've never heard anything specific on the topic.
nsob retweeted
Hi Astra users. A reset and a quick update on quality issues that have been posted around.
Working with some of you, we have found and fixed the following issues:
- Some skills written for previous models were triggering too often or preventing the model from checking its work.
- An opt-in context management experiment that could cause early stops or replies to older messages. We've disabled it. Our rough estimate is that 4-5k users were affected by this experiment.
- We've also removed some badly configured engines that resulted in a measured quality degradation for a long tail of traffic flowing through them.
We’ve also made some more minor improvements and things should feel significantly better across the board. More consistent follow-through, better tracking of your latest message, and better checks on the work as it’s going through the motions.
The examples posted and all the users who worked directly with us were incredibly useful in helping fix things quickly. Always grateful for this incredible community.
And of course, a reset is also landing by midnight today.
nsob retweeted
Anthropic 最让我反感的一点,是它真的觉得只要自己认为是在做好事,就可以替用户做很多决定。
它刚发了一份 154 页的 Threat Intelligence Report,里面整理了 Claude 被用于网络攻击、诈骗、生物研究、武器开发、模型蒸馏等案例。
这些案例是怎么来的?
Anthropic 会看用户怎么使用 Claude,发现它认为有问题的账号之后继续调查,分析这些人的使用方式,然后判断这个用户有没有越线。觉得有风险,就封号。之后再把这些事情整理成报告,对外解释它发现了什么、阻止了什么。
而且这种事情 Anthropic 已经做过很多次了。它会拿真实用户和 Claude 的对话去研究人到底在和 AI 聊什么,人怎么依赖 AI,人的价值观是什么,人在什么情况下会产生某种情绪。
它会告诉你,数据已经做过隐私处理,这些研究也是为了让 AI 更安全。
但我觉得这恰恰很傲慢。
为什么只要你说一句「为了 AI 安全」,你就可以去看用户在做什么?
为什么你觉得这件事值得研究,用户的数据就可以被拿去研究?
如果它觉得某种投资行为风险很高,某种政治讨论可能造成伤害,某种成人内容不符合它的原则,某种 AI 研究太危险,它是不是一样可以看、可以限制、可以封?
按照 Anthropic 现在这套逻辑,当然可以。
因为它一直默认了一件事:
Anthropic 比用户更知道什么事情应该做,什么事情不应该做。
这也是为什么我一直不信 Anthropic 那套 AI Safety 叙事。
一个天天说自己担心 AI 替人类做决定的公司,自己却已经越来越习惯替用户做决定了。太老登了,老登是注定要被打败的。
We're publishing our most detailed threat intelligence report to date.
It covers how people tried to misuse Claude—for cyberattacks, influence operations, surveillance, biology, and building weapons—and how we found and stopped them.
We disrupted every operation in the report, and used the lessons from them to strengthen our safeguards. Where appropriate, we also shared what we found with authorities and other AI companies.
These cases are not typical: we’re highlighting some of the most sophisticated misuse we’ve seen. But they’re especially important to discuss, because they show us where AI misuse is headed, where our safeguards work, and where they need to improve.
We’re publishing this report so others can spot the same activity on their own platforms, and so we can give the public a clearer view of how emerging threats develop.
Read the report: anthropic.com/threat-intelli…
nsob retweeted
我去,DeepSeek这波是真杀疯了,还是那个13B激活的便宜Flash模型,参数半毛钱没加,纯靠后训练就直接把代码Agent能力干涨7倍,
价格还是地板价,
都快摸到Opus的边了!!!
今天正式开公测的V4-Flash-0731,看完成绩单我直接傻了:
▫️ DeepSWE榜从7.3直接飙到54.4,翻了快7.5倍,之前的V4 Pro预览版才12.8,现在Flash直接把老Pro按在地上打
▫️ Cybergym从38.7干到76.7直接翻倍,Terminal Bench冲到82.7,比GLM-5.2还高1.7分
▫️ 工具调用、全栈开发、终端操作这些Agent核心场景,大部分指标都追平甚至超过了贵好几倍的中大型模型,部分硬榜已经摸到Claude Opus 4.8的尾巴
最狠的是,这次半毛钱没加参数:还是284B总参、13B激活的MoE,1M上下文窗口没变,纯靠后训练和Agent框架优化就出了这效果。
模型ID都不用改,老用户API直接自动切新版,一行代码都不用动。
开发者最爽的点全给你安排明白了:
1️⃣原生支持Responses API格式,直接适配Codex,之前接OpenAI、2️⃣Anthropic格式的代码无缝迁移,思考模式照常能用。
3️⃣价格还是那熟悉的白菜价:输入$0.14/百万token,缓存命中才$0.0028,输出$0.28/百万,跑高频Agent、批量改代码、工具调用根本不心疼。
另外提个醒:这次更的只有Flash API,V4 Pro正式版还在赶,App和网页端暂时没更;
跑分用的是官方即将开源的Harness,第三方框架跑出来可能有点差异,但这提升幅度,做执行层模型、跑自动化代码任务的,现在就可以开测了。
之前大家都觉得模型能力涨就得堆参数、烧算力,DeepSeek这波直接给行业打了个样:把后训练和Agent对齐磨到位,小激活模型照样能干旗舰的活,还卖白菜价。
我觉得这波之后,其他家的执行层模型,价格怕是又要往下掉咯
#DeepSeek #AI编程 #Agent #大模型
🚀 DeepSeek-V4-Flash Official API is now LIVE in public beta!
🔷 We’ve massively upgraded its Agent capabilities—benchmark scores are now far surpassing the V4-Pro-Preview. Check out the massive performance leap below! 👇
🔷 The official V4-Flash now natively supports the Responses API format and is fully adapted for Codex!
Check out the configuration details in our official API docs: api-docs.deepseek.com/quick_…
nsob retweeted
🚀 DeepSeek-V4-Flash Official API is now LIVE in public beta!
🔷 We’ve massively upgraded its Agent capabilities—benchmark scores are now far surpassing the V4-Pro-Preview. Check out the massive performance leap below! 👇
🔷 The official V4-Flash now natively supports the Responses API format and is fully adapted for Codex!
Check out the configuration details in our official API docs: api-docs.deepseek.com/quick_…
nsob retweeted
感觉现在开源的速度已经疯了。。。
@Kimi_Moonshot 家的K3开源才两天,已经被@UnslothAI 干到能在Mac Studio上跑了🤯!!!
2.8万亿参数的模型,原始大小1.56T,
他们用动态1-bit量化,直接压到594G,缩了62%,
还保留了78.9%的精度,
128G内存的Mac Studio就能跑,
虽然速度不快,但它真的能跑。
谁能想到半个月前,这个级别的模型还只有大厂有算力碰,
现在我们在家,在自己的消费级电脑上,就能跑全球最顶尖的开源大模型了,damn!
权重放出来48小时,量化、适配、教程全给你整完了,
Unsloth这速度,真的离谱。
不得不说这就是开源最恐怖的地方啊,
模型放出来第一天,
全世界最聪明的工程师都在帮你压体积、提速度、做适配,
闭源模型,永远享受不到这个待遇。
🤖 Made with AI
Kimi K3 can now be run locally! ✨
The 1-bit model retains ~78.9% accuracy after we shrunk it from 1.56TB to 594GB (-62% size).
Run on a Mac Studio + 128GB RAM device.
Kimi K3 is the strongest open model to date.
Guide: unsloth.ai/docs/models/kimi-…
GGUF: huggingface.co/unsloth/Kimi-…
nsob retweeted
Anthropic maintains that it is entitled to train for free on all the world’s output, even if the author objects. But if a competitor trains on Anthropic’s output after paying for it, that is IP theft. The hypocrisy is breathtaking.
btw anthropic's internal document on this literally said "we don't want it to be known that we are working on this.”
it was called project panama.
here's exactly what happened:
1: anthropic concluded that books were the cheapest way to build a world-class model because they gave claude curated facts, structured arguments, compelling stories, and writing “an editor would approve of.”
2: once anthropic decided it needed books at enormous scale, its first solution was piracy.
it downloaded 7m+ books from online libraries including libgen. the judge later wrote that although anthropic had legal ways to buy them, it chose piracy to avoid what dario amodei called the “legal/practice/business slog.”
3: that piracy created a massive legal risk.
so in february 2024, anthropic hired tom turvey, the former head of partnerships for google books, to find a legally safer way of obtaining “all the books in the world.”
4: turvey first contacted major publishers about licensing their catalogs.
those attempts didn’t produce agreements, so anthropic chose a route that required no publisher permission: buying millions of physical books through distributors and used-book retailers.
5: within about a year, anthropic spent tens of millions acquiring and scanning millions of books, including many rare and 1/1 titles. one vendor proposal targeted 500,000 to 2 million books in six months.
6: to scan that many books within months, the vendors physically dismantled them.
a hydraulic cutter removed each spine. the pages were trimmed to size, fed as loose sheets through high-speed industrial scanners, and converted into searchable PDFs. the paper remains were then sent for recycling.
7: these PDFs were fed into claude as training data.
the complete collection became a private, searchable anthropic library that the company planned to “store forever.” the scans aren’t available to the public and were never open-sourced.
nsob retweeted
我操!@kimi_moonshot 月之暗面这波是真的疯了!
2.8T 参数的 K3 直接全权重开源就算了,
连推理内核、MoE 通信库、Agent 训练环境整套底层技术栈全给你端出来,硬生生把开源模型的天花板,直接抬到了闭源旗舰的家门口。
不是放个权重就炒开源概念的挤牙膏操作,是从模型到底层能跑起来生态的所有东西全白给,1M 长上下文、MoE 架构性能够打闭源,
之前天天喊开源追不上闭源的直接被打半残,
开发者再也不用对着闭源 API 的限流、涨价受窝囊气。
搞大模型的不用从零搭底层通信框架,
做 Agent 的不用卡闭源接口的脖子,
小团队也能基于全栈直接做二次开发,
从今天起,全球开源 AI 的游戏规则,真的改了。
今天之后,开源和闭源的代差,从两年砍到了半年。
Kimi K3 今天正式全开源,不是挤牙膏的版本更新。
2.8T 总参数 MoE, 激活 104B, 原生 100 万上下文。
靠 KDA 新注意力和 AttnRes 架构创新,单位算力智能密度直接提 2.5 倍,长上下文解码速度翻 6 倍。
最狠的是,这根本不是只放个权重的假开源。
FlashKDA 高性能内核,MoE 通信库,连用来做 Agent 强化学习的快照式训练环境 AgentENV, 全给你开源了。
从推理到训练的整个底层栈,直接搬空。
之前所有开源模型,本质都是在追闭源一年多前的尾巴。
这是第一个真正摸到闭源顶尖门槛的开放模型。
长编码、Agent、终端操作这些硬核任务,
已经能和第一梯队闭源掰手腕。
你永远可以相信开放的力量!!
用K3搓了一个他自己开源的介绍视频,特此致敬🫡
Releasing the model weights and technical report of Kimi K3.
Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window.
New model architecture: 2.5x the intelligence per unit of compute, not just more params.
Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale.
Model weights: huggingface.co/moonshotai/Ki…
Tech report: github.com/MoonshotAI/Kimi-K…
Tech blog: kimi.com/blog/kimi-k3
nsob retweeted
Releasing the model weights and technical report of Kimi K3.
Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window.
New model architecture: 2.5x the intelligence per unit of compute, not just more params.
Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale.
Model weights: huggingface.co/moonshotai/Ki…
Tech report: github.com/MoonshotAI/Kimi-K…
Tech blog: kimi.com/blog/kimi-k3
The people at Anthropic are either stupid or malicious. Their actions are exactly what you'd expect from people with their mindset.
I’m so excited that @JensenHuang is a believer in open source now, looking forward to the CUDA and GPU driver open source release!
nsob retweeted
For my first post, I’m sharing a letter @NVIDIA signed on why open models matter.
AI will transform every industry, power every company, and be built by every country.
Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty.
The world needs both frontier closed models and frontier open models.
images.nvidia.com/pdf/Open-W…
刚刚看完DeepSeek创始人最新的《梁文锋四小时投资人会议》全文。
这是我看过最长最爽的科技爽文。
把我对科技的兴奋与激情再次被点燃。
我给大家把所有的内容用图文提炼出来,大家也可以爽一爽。
梁文峰最新发言核心要点(精简)
这场四小时会议,没有讲融资、没有讲估值、没有讲上市,而是反复回答一个问题:
DeepSeek为什么存在?
答案只有两个字:AGI。
⸻
1、AGI是唯一主线
梁文锋认为,产品、流量、商业化都只是阶段性的,真正值得投入全部资源的只有AGI。
ChatBot、Agent、多模态,都只是通向AGI的台阶,而不是终点。
⸻
2、下一代AI,不是更会推理,而是会持续学习
现在的大模型每次工作都要重新输入上下文,本质上还是”不会成长”。
真正的突破是:
持续学习 → AI研发AI → AI自我迭代 → 具身智能 → AGI。
一旦AI开始研发下一代AI,智能演进速度将进入全新阶段。
⸻
3、Coding Agent是现阶段最重要的方向
金融、医疗、法律Agent都可以做,但优先级都不如Coding Agent。
因为只有Coding Agent,才能帮助AI开发AI,这是迈向AGI最快的路径。
⸻
4、低成本不是价格战,而是技术实力
DeepSeek不断压低成本,不是为了卷价格,而是为了提高计算效率。
成本越低,同样算力就能训练更大的模型、服务更多用户。
未来模型竞争,成本将排在第一位。
⸻
5、坚持开源,是战略,不是情怀
梁文锋认为,如果AGI真的拥有巨大的商业价值,就没必要独占全部利益。
开源能够聚集全球开发者、吸引顶尖人才、建立生态,也能提高实现AGI的成功概率。
⸻
6、不争流量,不做超级App
DeepSeek无意成为下一个腾讯,也不想成为下一个字节跳动。
别人争ChatBot、争C端、争ToB,在梁文锋看来,都只是眼前的市场。
真正的大机会,还在AGI。
⸻
7、商业化不是第一目标
DeepSeek一直在商业化,但目标不是利润最大化,而是赚”合理利润”。
公司更愿意把资源继续投入研发,而不是追求短期收益。
⸻
8、未来赢家不会太多
梁文锋判断,大模型行业最终一定会收敛。
真正决定胜负的,不是参数规模,而是:
成本
速度
用户体验
其中,成本排第一。
⸻
9、最大的资产不是GPU,而是团队
他认为,公司最大的风险不是资金、不是算力,而是团队稳定。
因此,DeepSeek强调研究自由、减少KPI、不鼓励长期加班,希望让优秀研究者长期专注创新。
⸻
最后一段,几乎概括了DeepSeek的全部哲学
梁文锋相信:
克制,比扩张更重要;
效率,比规模更重要;
长期主义,比短期利润更重要。
不追风口,不追流量,不追超级App,不追利润最大化。
只做一件事——全力冲向AGI。
这或许也是DeepSeek与大多数AI公司最大的不同:别人想赢下一场竞争,而它想定义下一个时代。































