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Tracking AI & Technology With Caduceus: 🔗 https://teapawt.org/t.co/xPOhLouCXA

@caduceusvisuals
Joined September 2021
Photo by Apollo (@ApollonVisual)Photo by Apollo (@ApollonVisual)Photo by Apollo (@ApollonVisual)Photo by Apollo (@ApollonVisual)Photo by Apollo (@ApollonVisual)Photo by Apollo (@ApollonVisual)Photo by Apollo (@ApollonVisual)Photo by Apollo (@ApollonVisual)Photo by Apollo (@ApollonVisual)Photo by Apollo (@ApollonVisual)
Apollo retweeted
I actually thought the personal security expenses for Dario or someone else at that level of AI leadership will be much larger. Fortune: Anthropic’s S-1 shows the company spent $2.75M on Dario Amodei’s personal security in 2025, more than 4 times his $654,167 salary and more than 15% of his $17.96M total pay. The protection extended to president Daniela Amodei at $1.29M, and Dario’s security program also covered $97,055 in private-aircraft travel. By big-tech standards the bill is moderate, at roughly one-eighth of Meta’s $22.5M for Mark Zuckerberg, yet it is nearly 7 times the $401,336 median for software and services CEOs.
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Apollo retweeted
Today, we release the weights of a new and improved Le Chonk, advancing the open frontier for agentic coding and cybersecurity. 💻 Built to build: Le Chonk ranks #1 on @cognition’s FrontierCode among open models, and sets a new benchmark for Pareto efficiency on @artificialanalysis. 🛡 Built to defend: State-of-the-art in cyberdefense, put through the paces by the world's leading cyber vendors, including @CrowdStrike, @cloudflare, @rubrik, @zscaler, and others. Built for sovereignty: Make it yours. Download the weights on @huggingface , customize it with Mistral Forge, and deploy it wherever you need it. Read more from our team and customers 👇:
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Apollo retweeted
Three new identifiers, deepseek-v5, deepseek-v5-open and deepseek-v5-next, appeared in test code in DeepSeek's official Harness repo. They are placeholders and DeepSeek has not announced V5. @deepseek_ai
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Apollo retweeted
Moore Threads open-sourced Megatron-MUSA-Titan, its training stack for trillion-parameter MoE models on 10,000+ GPU clusters, covering pretraining and RL with blockwise FP8. Code is on GitHub.
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Apollo retweeted
Kimi now works with Gmail, Google Drive, and Google Calendar. Summarize Gmail threads, find files in Google Drive, and plan your day with Google Calendar — with secure OAuth access you control. Try them at kimi.ai/plugins
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Microsoft released GitHub Copilot Fusion, which routes coding work between Claude and GPT models and landed just points below Claude Code at half its cost per task. On Artificial Analysis’s Coding Agent Index, its Max setting scored 60 against 62 for Claude Code, at $5.59 per task versus $11.42. Fusion also used 7.2M tokens per task, roughly half Claude Code’s usage.
Just wrapped our earnings call. It was a fast start to our fiscal year. Microsoft Cloud revenue surpassed $63B, with growth accelerating to 30% YoY. Lots of innovation as we build the new tech stack for the agentic wave. A few highlights:
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New image model coming up from @OpenAI: "GPT-Image-2.6" This should be rolling out soon.
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Apollo retweeted
Drex Max 1.1 is live 🦖 SOTA decision model with significantly improved out-of-distribution generalization - Multimodal - 262k context window - $0.03 / 1M input tokens (50% launch discount) Grab your API key: console.nace.ai
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NVIDIA released Nemotron 3.5 Transcribe, a new open-weight, 30B MoE model with ~4B active parameters. > Nemotron 3.5 Transcribe can transcribe in 40 languages, perform speaker attribution, translate, handle contextual prompting, and understand audio. > It ranked first in VoiceArena’s 14-system diarization + ASR comparison.
Meet NVIDIA Nemotron 3.5 Transcribe 🎙️ An open-weight, promptable 30B MoE model with ~4B active parameters that brings transcription in 40 languages, speaker attribution, translation, contextual prompting and audio understanding into one checkpoint. Watch it keep track of who said what when everyone’s talking at once. 1/ 🧵
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China targets over 22 high-value invention patents per 10,000 people by 2030 China aims to increase the number of high-value invention patents per 10,000 people to more than 22 by the end of the 15th Five-Year Plan period (2026–2030), up from 16 at the end of 2025, according to the intellectual property plan recently issued by the State Council. The target reflects a shift from patent quantity toward quality, with greater emphasis on innovation value and practical applications. Focusing on high-value innovation China classifies high-value invention patents into four categories: those in strategic emerging industries, those with corresponding patents granted overseas, those maintained for more than 10 years, and those that have received national science and technology awards or China Patent Awards. More than 70 percent of China's high-value invention patents are in strategic emerging industries, including artificial intelligence, advanced manufacturing, integrated circuits, biomedicine and new energy. This share is expected to rise further during the 15th Five-Year Plan period, supporting the development of emerging industries and the cultivation of future industries. To improve patent quality, China is encouraging universities and research institutions to assess inventions before filing patent applications. More than 1,200 universities and research institutions have established pre-application evaluation systems to improve application quality and direct innovation resources toward key fields. Enterprises drive innovation Enterprises are major contributors to high-value invention patents. By the end of 2025, about 280,000 Chinese companies held 1.764 million such patents, accounting for 77 percent of the national total. As businesses invest more in research and intellectual property, high-value patents are helping drive technological innovation and strengthen market competitiveness. Feedback from commercial applications can also help align research with industry needs. Turning patents into practical results Alongside efforts to improve patent quality, China is promoting the commercialization and practical application of intellectual property to better translate research into economic and social benefits. By the end of 2025, the average examination period for invention patents had fallen to 15 months. In 2025, technology contracts involving patents reached a total transaction value of 1.18 trillion yuan, up 18.8 percent year on year. During the 15th Five-Year Plan period, China plans to further strengthen intellectual property protection and improve the commercialization of patent results, supporting technological innovation, industrial development and high-quality economic growth.
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中国社区逆向拆解了Claude的封号机制。以下是他们的发现 Claude使用积分制。注册时账号获得初始分数,之后根据行为加分或扣分。低于阈值直接封号。回分很难 影响分数的因素: 注册 > 干净IP是最重要的因素。不一定要家宽,但必须干净 > Gmail已经不是最优选,有权重更高的邮箱服务 > 注册时直接弹KYC的号可以扔了 > 用手机App注册会加分 支付 > 刚注册就订阅会扣分 > 直接上Max会大幅扣分。最好等两天先订Pro > 支付时弹KYC说明账号在考察期,但不一定会封 > 国外实体银行卡加分。Google Play也行。虚拟卡扣分 使用 > 不要一次把限额用完。宁可两个号轮换也不要一个号撸到底 > Desktop客户端有少量加分。尽量少用网页端 > 手机端也有少量加分 > 开启记忆功能加分。记忆文件越多分越高 IP和设备 > 稳定性是关键。不要跳IP,尽量一直用同一个 > 代理必须全局模式并开启TUN > 多设备登录大幅扣分。理想状态是一台电脑一个客户端 > 频繁换IP、频繁登录、高并发都会大幅扣分 账号年龄 > 老号有巨大的容错空间。2023年的号明显更高 > 分数随时间增长,但很慢 其他 > Windows和macOS没区别。Linux会扣分 > 时区和语言基本不影响。不会因为北京时间就封号 > 跟Claude说自己是中国人没问题,记忆不会触发封号 > 大量使用Fable会扣分 > 升级Max大概率进入观察期,除非是高权重老号 观察期和封号 > 接近阈值时账号可能进入观察期。典型特征是掉登录 > 观察期继续扣分就会封号 > 高分号可以扛过多次观察期 关注我,拆解你每天在用的服务背后到底怎么运作的
这篇估计没多少人会认真看完,我就当给未来的自己留个记录。几年后再回来看看,今天我们是怎么给 AI 付费、怎么搭自己的 AI 工具栈的,可能会比现在更有意思。
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As expected, Kimi 3.1 Pro distilled US frontier models to train their models. This time they distilled GPT-6 Astra. The damage to Kimi’s reputation keeps growing. Via the information
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Apollo retweeted
Today we're announcing support for university research at @Caltech, @OhioStateMed, @LSU, @IndianaUniv, and @UTHealthHouston to help advance breakthroughs across science and medicine. As part of the support for university researchers, we’ll provide access to our latest Muse models, including Muse Wonder and Muse Spark; open weight models like Muse Glimmer and Muse Spark Open; computer vision models such as SAM, V-JEPA, and DINO; as well as SeamlessM4T, our multilingual communication model. We’re excited to support these five universities as they use our models to accelerate the important work they’re doing. Read more on our research blog: go.meta.me/47G6wV3
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Replying to @GoogleDeepMind
#1 in Artificial Analysis Intelligence Index v5 and #2 in Artificial Analysis Coding Agent Index teapawt.org/ArtificialAnlys/status…
Google’s Gemini 4 Carbon is the new #1 on the Artificial Analysis Intelligence Index v5, and second only to Claude Opus 5.5 on the Artificial Analysis Coding Agent Index Gemini 4 Carbon scores 59 on Intelligence Index v5, driven by leading performance in complex reasoning, knowledge, and scientific tasks. It also has large gains in coding and knowledge work, particularly when compared to other Gemini models, but it is not leading in all these areas. As with launch of Gemini 4 Argon, Gemini 4 Carbon is being rolled out to selected users and is not yet publicly available. Google is offering a 50% launch discount, with no confirmed end date. All our charts and figures use standard pricing for both Gemini 4 Carbon and Argon. Key results for Gemini 4 Carbon with high reasoning: ➤ Gemini 4 Carbon leads the Intelligence Index v5, with the #1 score in 4 of the 9 Index evaluations: HLE-Diamond, Terminal-Bench-Science, AA-Omniscience, AutomationBench-AA. Carbon makes substantial gains elsewhere but is not leading in coding and knowledge work ➤ Behind only Claude Opus 5.5 on coding agents: With Antigravity CLI, Gemini 4 Carbon (high) scores 59 only 3 points behind Claude Code with Claude Opus 5.5 (max, 62) on the Artificial Analysis Coding Agent Index. Carbon ranks second on both Terminal-Bench 4.0 (64%) and FrontierCode v1.1 (51%), behind Claude Opus 5.5 (max) at 56%. Google has historically underperformed in this area and so this improvement stands out ➤ Improved agentic knowledge work, but not yet leading: On AA-Briefcase, Gemini 4 Carbon (high) scores 1609 Elo, a 126-point improvement over Gemini 4 Argon (high, 1483), but still behind models including Grok 4.7 (xhigh, 1638), Muse Wonder (max, 1659) and Kimi K3.1 Pro (1678). The largest gain comes from Presentation, up 293 Elo to 1598, alongside a 120-point improvement in Analytical Quality to 1671 ➤ Lower Cost per Task than Claude Opus 5.5 for comparable overall intelligence: Gemini 4 Carbon costs $7.76 per Intelligence Index task, ~23% less than Claude Opus 5.5 (max, $10.04) but ~1.2x GPT-6 Astra (max, $6.25). It uses 123k output tokens per task, 29% fewer than Claude Opus 5.5 (max, 173k). At the same $4/$20 standard pricing, its Cost per Task is in line with Gemini 4 Argon ($7.68), while scoring 9 points higher on the Intelligence Index ➤ Leading AA-Omniscience v1.1 score, with low hallucination: Gemini 4 Carbon scores 54 on our recently updated AA-Omniscience v1.1, 6 points ahead of Claude Opus 5.5 (max, 48) and 8 points ahead of Gemini 4 Argon (46). Accuracy (% correct) rises 10 points over Argon to 58%, while its hallucination rate rises from 4% to 11%. This is still the second lowest of any model, behind only Argon, compared with 50% for both Claude Opus 5.5 (max) and GPT-6 Astra (max) Key model details: ➤ Context Window: 1M tokens ➤ Multimodality: Text, image, video, and speech input, with text output ➤ Pricing: $4/$20 per 1M input/output tokens, the same as Gemini 4 Argon’s standard pricing, with a 50% discount. Cached input tokens receive a 95% discount ($0.20 per 1M) Congratulations to the @GoogleDeepMind team!
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Gemini 4 Carbon is our next frontier model. It’s state-of-the-art across cybersecurity and long-horizon capabilities.
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Apollo retweeted
New in Claude for Legal: a gallery of ready-made artifacts for contract review, regulatory horizon scans, cite checks, and more. Start from the built-in example, add your own documents and connectors, and share it with anyone across the business.
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Apollo retweeted
Grok Imagine
Our Prompting Guide is live. We're building the Odyssey episodes in public with a small crew of creatives. Learn from our workflows!
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Meet Clover-35B-A3B-Preview 🍀 the first open-weight production diffusion language model (dLLM) with 35B total parameters capable of 680 tps on a single card at BF16. Clover exhibits strong performance against both dLLM and AR models and is available today at @huggingface @ModelScope2022 and at our website
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We're expanding our partnership with @USGSA. Under a new OneGov agreement, we are bringing Claude to federal, state, local, tribal, and territorial governments at 40% off. anthropic.com/news/expanding…
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