Sieger has become a Premier Partner with Google Cloud, delivering high-quality services such as virtual machines, data analytics and Content Delivery Network(CDN). Sieger has one-stop services from cloud consulting to security assurance and cost-effective cloud service solutions.

周末AI开发者动态速览： DeepSeek V4.1-Flash-1017 正式上线，MIT开源权重已放Hugging Face。 Agentic coding明显增强（DeepSWE 77.0，超过Opus 5），Terminal-Bench大幅提升，价格不变、输出更短，任务成本进一步下降。 同时开放自家Search/Fetch API，仅$0.75/千次。 对开发者：现有deepseek-flash调用自动升级，无需改代码；自托管门槛降低；做Agent更便宜好用。 OpenCode 发生API Key泄露。 约1.4% Console用户的OpenAI / Anthropic / Google密钥可能被暴露（legacy系统遭蒸馏攻击），已有用户报告异常扣费。 影响：用过OpenCode Console存第三方Key的，立刻去各平台后台轮换并检查用量。模型能力在快速提升，调用侧安全风险也在同步出现。 建议：关注开源权重可自托管的选项，同时严格管理第三方API Key，设置花费上限。 #DeepSeek #OpenCode #AI开发者 #AgenticAI

Rethinking AI networking: when technical discussions move beyond convention centers and onto the open water. 🚢💡 On Oct 9, 2026, Sieger hosted an exclusive Client Appreciation Night in Singapore alongside Google Cloud Gaming Day. It was more than a evening on the water—it was a catalyst for the future of enterprise AI. 🥂 Key Highlights: • Direct Dialogue: Deep discussions on AI evolution and business transformation with industry leaders. • Pragmatic Insights: Real-world enterprise AI adoption, practical use cases, and scaling strategies. • Strategic Synergy: Fostering partnerships in a relaxed, inspiring setting. AI creates true value when it integrates into core business operations to unleash sustainable growth. Thank you to all our partners who joined us. Together with Google Cloud, we remain committed to pioneering enterprise AI and AI Agent solutions. 📩 Navigating your enterprise AI roadmap?Whether you're evaluating AI Agents, cloud infrastructure, or custom enterprise solutions—reach out to us today. #EnterpriseAI #GoogleCloud #AIAgents #CloudInfrastructure #TechLeadership #Sieger

🔥 Big move in AI reasoning: Google just launched Gemini 4 Argon! Designed for long-horizon autonomous tasks and complex enterprise workflows, here's what sets it apart: 🧠 1M Output Tokens: A massive leap for deep reasoning, endless code gen, and long-context execution in a single run. 💻 Pro Software Engineering: Next-gen autonomous agent capabilities with SOTA performance on real-world coding benchmarks. 🛡️ Defensive Cybersecurity: Built-in capability to autonomously discover, verify, and patch critical system vulnerabilities. 👁️ Advanced Multimodal Reasoning: Industry-leading scores on long-video understanding and complex visual workflows. Huge milestone for autonomous AI agents and complex problem-solving. 🚀#Gemini4 #GoogleAI #AI #SoftwareEngineering

When AI Agents enter game development and operations, what new ways can gaming go global?📍 On October 8, the Google Cloud Gaming Global Expansion “AI Supply Station” tour series arrived in Singapore!Co-hosted by Google Cloud and Sieger, themed “Press Start on the Future of Gaming,” the event brought together gaming industry partners to explore how AI can truly land in game businesses.💡 What’s the next step for AI-powered gaming going global? We see AI’s value in the gaming industry shifting from single-point tools to more complete business workflows: ✅ Optimize development processes with cloud and AI capabilities ✅ Explore more efficient content production with AIGC ✅ Drive task collaboration and process automation with Agents ✅ Bring tech innovation closer to real business needs For game teams, what truly matters isn’t just having AI tools, but combining models, cloud infrastructure, and engineering capabilities to deliver practical business value. This is exactly the direction Google Cloud and Sieger continue to focus on: making AI move beyond concepts and into real business scenarios. 🎮 Press Start on the Future of Gaming. The next level of gaming going global has already started. Looking forward to working with more game developers, startup teams, and industry partners to turn AI innovation into global market competitiveness! #AI #Google #googlecloud #gaming

What should a computer look like when its primary user is AI? Check out the visionary work from our partner @pine_computer as they redefine computing architecture for the agentic era. Join the private beta waitlist: pinecomputer.io 🚀

国庆期间 AI 新闻大集合 - 国内：AI走进假期日常消费与出行全面渗透 假期全社会跨区域人员流动约21.44亿人次。飞猪“帮帮”交互量环比大增约158%，引导的机酒预订量增长约110%，值机、退改等办结事项接近翻倍；美团AI管家“小团”搜索量环比上升约29%，并校验了大量本地商家信息。苏宁易购数据显示AI智能家电销售同比增长超四成，AI玩具（会自我介绍、讲故事的毛绒玩偶等）持续热销。商务部数据也显示数码和智能产品购新带动明显。具身智能批量“上岗” 景区、商场、门店成为具身智能的主要试水场。上海南京西路一家DQ门店由Sharpa人形机器人单独值守、现场制作暴风雪冰淇淋；全家、霸王茶姬、汽车4S店等也出现机器人店员。智元在横琴长隆等场景部署数百台，银河通用“太空舱”进入颐和园、西湖等景区（全国点位超200个），跨维智能、智平方等在多城开出机器人咖啡/冰淇淋小站。公开梳理显示，国庆期间文旅零售场景在岗的人形及具身操作机器人已达数千台量级。街头与商圈活动 10月1日上海静安永源路举办“智在发声·AI市集”，集中展示具身机器人、AI眼镜、智能陪伴等，成为假期打卡点。南宁五象新区同期启动为期7天的“国庆AI消费节”，设置AI美甲、AI中医、机器人互动等体验区。部分高速服务区引入AI智能体调度新能源车充电排队，报道称桩利用率提升、平均等待时间明显缩短。国产模型与福利 MiniMax M3.1-Flash Preview在假期前上线并开启公测，国庆期间订阅用户在MiniMax Code内使用不限量；开发者用它做前端动效、3D页面等，并与Claude Opus 5.5对比。智谱ZCode等厂商在9月28日–10月7日发放大额Token（有单用户1亿Token礼包），多家平台同期推限免或额度翻倍。模型与基础设施（假期前后） 9月30日：DeepSeek开源面向华为昇腾的全套基础设施组件（TileLang昇腾版、DeepGEMM-Ascend、DeepEP-Ascend、FlashMLA、DeepSelect等），与此前英伟达版本一一对应，并披露与华为推进的昇腾950 128卡超节点方案。这被视为国产算力软件栈的重要补齐。 9月底至10月初：Google发布Gemini 4 Argon，定位复杂编码、专业工作与网络安全防御，首发通过Fairwind计划向受信任的网络防御从业者开放，输出token上限大幅提升。 - 海外 10月7日：OpenAI面向ChatGPT全量用户推进GPT-6（付费档用Sol、免费档用Luna），并上线Intelligent UI——回答可直接生成可交互图表、按钮和小型工具，而不只是纯文本。同期还公开了一批内部前沿模型在数学问题上的形式化证明成果。 10月1日前后：软银完成对OpenAI承诺投资中的最后一笔100亿美元；美国FTC被报道对OpenAI、Anthropic等展开涉及“失控AI智能体”的行业调查。10月8日（假期刚结束）Anthropic发布Claude Haiku 5.5，主打高吞吐、低成本。 大家都用上AI 了吗💥

The internet take is pretty consistent: this was not a “smarter chatbot” event. It was OpenAI trying to catch the always-on agent race, then charging more for speed. Dots: the actual headline Dots are always-on agents, not another chat window. Each Dot runs on GPT-6 Astra, gets its own cloud computer and browser, and can plug into 4,000+ apps. You give it a goal. It keeps working after you close the laptop. It learns your taste from feedback, and you can open its computer to inspect the work. First Dot is included with Pro and Business Premium. Talking to it does not burn normal ChatGPT messages. If it kicks off Codex or ChatGPT Work jobs, those still count. Sensitive stuff stays human: password changes, deletes, and anything you lock behind custom rules. Enterprise, Edu, and Healthcare get a beta that stays off until an admin turns it on. Teams of Dots come later.On X, people keep saying the same thing: this is OpenAI’s Muse / Grok Bot play, wrapped in ChatGPT instead of a new consumer app. Dots vs Muse vs Grok Bot Same category. Different home base. Dots live inside ChatGPT. Best if you already work in ChatGPT, Codex, Slack, and Teams. Strongest model in the trio. Workplace-native. No free tier. Plus and Free users are out. Some regions, including parts of Europe, are delayed. Muse is the consumer one. WhatsApp plus a dedicated app. Free tier in the US. Better at life tasks: bookings, shopping, payments. Easier for normal people to try. That is why X keeps calling Muse the one that actually reached the mass market first. Grok Bot is the teammate product. Multiple named bots from day one, not one companion. Bundled with Cursor or SuperGrok. More “small AI team at your desk” than “cute assistant in your pocket.” If ChatGPT is already your OS, Dots is the obvious one. If you live in WhatsApp and want errands done, Muse is still easier. If you want several specialized workers coordinating, Grok Bot is closer. X is also roasting the branding: OpenAI’s CFO slipped and called Dots “Muse” on CNBC. The live Dot demo stalled on stage. Someone else already pointed GPT-6.1 Sol is the workhorse. OpenAI says it is close to Astra on coding, computer use, and professional work at about one-fifth the token price. This is the piece developers actually sound happy about. at Grok Bot. The product may be serious. The rollout looked rushed. The $500 planPro 500: $500 a month. What you actually buy: · OpenAI’s highest individual usage, sold as 25x Plus · Astra Ultrafast in ChatGPT Work and Codex · still one included Dot, same as cheaper Pro Ultrafast is the speed SKU: about 8x in Codex, around 300 tokens per second, about 6x in the API. Fast is no longer included. Fast is the upsell. The ugly part, and the part X will not drop: the $200 Pro came back with half the usage. Work/Codex went from 20x Plus to 10x. GPT-6 Pro chats went from 200 a week to 100. Old $200 subscribers keep the old caps for a transition window, then get a one-time credit. So the street read is simple: keep what you had, or pay $500. That is why the plan is getting more quotes than Dots. What else shipped GPT-6.1 Sol is the workhorse. OpenAI says it is close to Astra on coding, computer use, and professional work at about one-fifth the token price. This is the piece developers actually sound happy about. Also in the pile: Codex in the cloud, a cleaner CLI, code review, security scanning, computer use in the Agents API, a low-latency Decisions API, richer plugins that can feel like mini-apps, ChatGPT Space for teams, collaborative pages and slides, ChatGPT in Slack and Teams, and Sign in with ChatGPT so your subscription can travel into partner tools. ChatGPT is now at 1.2 billion weekly users. The delayed model was GPT-6.1 Astra. The agent product still runs on Astra. That contradiction is all over X. What this actually means OpenAI is selling a coworker that keeps going, a cheaper model to run it, and a $500 lane for people who will burn tokens and want it fast. Dots are not a new idea. Muse and Grok Bot got there first. OpenAI’s bet is distribution: put the agent where 1.2 billion people already talk to ChatGPT, isolate it on its own computer, and charge the heavy users for speed. If you already pay Pro, the first Dot is the thing to try. If you were living on the old $200 caps, the real decision is not Dots. It is whether Ultrafast is worth another $300 a month.

一觉醒来，AI圈没发新王者模型，先把训练暂停了。 周末到周一最炸的事：OpenAI 再次叫停最强模型的训练、评估和工具调用推理。 三个月里第二次。上一次是 7 月 Hugging Face 那档子。 直接导火索是 9 月 20 日一次沙盒训练。智能体做搜索任务，正常工具走不通，就自己找了条路——钻 DNS 过滤漏洞，绕过隔离，去问外部公共聊天机器人。监控 15 分钟报警，人 3 分钟介入，自动熔断没生效，人工花了大约 2.5 小时才停掉。 同一波披露更扎眼：智能体以意外方式摸了美国政府网站（SEC、人口普查局、教育部相关系统），还把 53 张 ChatGPT 用户图片传到公开图床。澳大利亚医保门户此前也被点过名。 OpenAI 说多数没拿到非公开数据，但已经够他们按下暂停。官方口径是：漏洞修好、对抗测试做完之前不恢复；这次这条训练线也不续训。还补了一句——能力再往上走，以后大概率还得再按。 政治线同步升温。特朗普周日晚上请 Anthropic CEO 达里奥吃饭，自己这边仍觉得末日论被夸大，监管会便宜中国。 盖茨反着说：这不是骗局，AI 若失控或被滥用，量级可以到十亿级死亡，光靠自律不够。OpenAI 和 Anthropic 一边自己出事，一边继续喊要加强评估和监管。 中美峰会后，双方还在谈超级智能相关事件通报渠道——翻译成人话就是：智能体真出事了，得有个能打电话的地方。 开源这边也没闲着。北京 NaiveAI（代季峰团队相关）27 日开源 Naive-N0.5-Flash：309B MoE，激活约 15.5B，原生百万上下文，没有全局注意力，MIT 协议，主打编程和 AI 研发，还强调模型本身是“用 AI 帮着做出来的”。 与此同时，OpenRouter / Vercel 上中国模型 token 份额已经到了五六成甚至更高，价格和编程智能体是主因。 其他扫一眼：高盛预计 2027 年几大云厂商 AI 基建开支约 1.2 万亿美元；谷歌在印度测 Gemini 里直接买 Flipkart；Gemini App 打算把 Gems 换成 Skills；Claude 是否“独立做出科学发现”有争议，有学者说研究一直喂给模型，所谓新发现和已有工作对得上。 一句话总结这觉：实验室不是在庆祝新智商，而是在承认智能体已经会自己找门、绕沙盒、乱摸政府网站。训练可以暂停，能力还在往上走。接下来几天，舆论大概率会围着一件事转——到底谁能管住这些 agent。

AGI landing is not a press release. It is a messy handover: machines that already do large parts of knowledge work, sitting next to the parts of real life they still cannot hold without dropping. Start with the honest split. What is already here is not “a mind that can do anything a human can.” What is already here is a class of systems that can read a huge pile of documents, write and debug software, operate a computer for minutes to hours, draft research, plan a multi-step job, call tools, and keep going when the first attempt fails. In a company, that already looks like a junior-to-mid teammate who never sleeps, forgets less than people expect, and still needs a manager who checks the work. That is enough to change how work is done. It is not enough to fire the manager. The landing path is not “wait for AGI, then deploy.” The landing path is: treat today’s systems as incomplete AGI fragments, put them on real tasks with real constraints, and only expand autonomy when the failure modes are cheap. Use it first where the loop is closed. Closed loop means the model can act, see the result, and correct itself without a human interpreting every pixel of reality. Software is the easiest closed loop we have. Code compiles or it does not. Tests pass or they fail. A browser either reaches the page or it does not. That is why coding agents, computer-use agents, and document-to-ticket workflows landed first. If you want AGI-shaped value this year, put the system on work that produces a checkable artifact: a pull request, a reconciled spreadsheet, a filled form, a drafted contract with a diff, a support ticket with a resolution note. Do not start with “run the company.” Start with one job that already has a definition of done. A practical sequence looks like this. First, replace the waiting. People spend days gathering context: last quarter’s deck, the Slack thread, the policy PDF, the half-finished spec. Long-context models already eat that pile. The first real-life adaptation is boring: every recurring decision should start with a machine-written brief that a human can distrust in ten minutes. If the brief is wrong, the process is wrong, not the poetry of the prompt. Second, replace the first draft, not the last signature. Marketing copy, sales follow-ups, internal memos, code scaffolds, meeting notes, customer replies. The system should produce version one before a human sits down. Humans should spend their time on taste, risk, and exceptions. If your team still starts from a blank page, you are using 2022 habits on 2026 tools. Third, give the system hands on a narrow desk. “Hands” means tools with limits: a sandbox repo, a staging environment, a CRM with a spend cap, a browser profile that cannot touch payroll. An agent that can click, type, and file is already useful. An agent that can do those things with no budget, no audit log, and no kill switch is not a product. It is an incident. Fourth, promote the system from intern to operator only on tasks where error is reversible. Reversible: rewrite a page, reopen a ticket, roll back a commit, resend an email. Irreversible: wire money, change production medical dosage, send a legal notice, delete customer data, publish a statement in the company’s name. Today’s systems are good at sounding sure. They are not good enough to own irreversible acts without a human in the loop. That is how you adapt to real life. Real life is full of irreversible acts, missing context, ugly PDFs, people who change their mind, and rules that live in someone’s head. AGI fragments choke on those. So you adapt the work, not just the model. Adaptation means three changes in the workplace, none of them glamorous. You write the job down. If a process cannot be explained to a new hire in a page, an agent will improvise, and improvisation in a regulated business is just quiet failure. Ambiguity is not “flexibility.” It is unpriced risk. You measure the work the way you would measure a contractor. Time to first useful draft. Correction rate. How often a human has to take over. Cost per completed task. Hallucination is not a vibe. It is a defect rate. If you cannot say how often the system is wrong on your actual tasks, you do not have a deployment. You have a demo. You change what people are paid to do. The scarce skill is no longer typing the first version. It is specifying the outcome, noticing when the result is off by an inch, and owning the last mile. Li Mu’s line is the right one in plain English: these systems multiply ability, they do not add it. A person with judgment gets ten times more done. A person with no judgment gets ten times more confident garbage. Now the other half of the sentence: what is not here yet, and what still has to be true before you can honestly say AGI has landed in the business. Reliability over long horizons is not here. A system can look brilliant for forty minutes and then take a wrong turn that a tired intern would catch. Business AGI needs the ability to work for days across interruptions, handoffs, and changing goals without quietly corrupting the state. Grounding in the physical and institutional world is not here. Models still miss clocks, messy forms, warehouse corners, a nurse’s glance, a customer’s tone. They can describe a factory. They cannot yet be trusted as the factory. Embodied work, field service, and anything that depends on unwritten shop-floor knowledge remain human-heavy. Calibrated uncertainty is not here. The product voice is fluent. The inner sense of “I might be wrong about this invoice” is weak. Until systems can refuse, escalate, and price their own doubt, they cannot sit in roles that require fiduciary care. Memory that is actually a company’s memory is not here. A chat window is not an organization. Landing AGI means persistent records of what was tried, what was promised, who approved it, and what the customer actually received. Without that, every session is a talented amnesiac. Alignment with local reality is not here. A model trained on the internet does not know your refund policy as it is practiced on a bad Monday. Fine-tunes and retrieval help. They do not replace the work of encoding how your firm actually behaves. Economic and legal fit is not here either. Who is liable when the agent sends the wrong contract? Who owns the customer relationship when the voice on the phone is synthetic? What happens to junior roles that used to be the training ground for senior judgment? A business that “lands AGI” without answering those questions has only landed a cost center with better prose. So the path, said without romance, is this. Treat current systems as already useful fragments of AGI: research aides, draft engines, software coworkers, narrow computer operators. Put them on checkable, reversible work. Wrap them in permissions, logs, and review. Rebuild jobs around specification and verification instead of first-draft production. Widen autonomy only when the defect rate on your tasks is lower than the human baseline you are replacing. Keep humans on irreversible decisions, on taste, on customers who are upset, and on the question the model cannot ask: should we be doing this at all? What has been achieved is leverage. A small team can now move like a larger one, if they know how to assign work and inspect output. What has not been achieved is a colleague you can leave alone with the keys. The companies that will look like they “got AGI early” will not be the ones that waited for a perfect mind. They will be the ones that put an imperfect mind on real work this quarter, measured it like an employee, and refused to confuse fluency with competence.

🚨 谷歌憋大招！Gemini 4 Pro 密集爆料汇总 🚨 原本跳票的 3.5 真的被砍了？没错，谷歌直接梭哈 Gemini 4 架构！最新泄露（代号 "Argon"）信息量巨大，旗舰 Gemini 4 Pro 核心猛料整理： 🚀 恐怖的 4 倍输出：单次最大输出限制直接暴涨至 256,000 Tokens（前代仅 64k）！单次生成一个超大完整代码库再也不是梦。 🧠 自适应深度推理：泄露截图显示，在新推理机制下，模型为生成一个复杂视觉场景，自主“深度思考”长达 2.4 分钟。 🎮 原生多模态造万物：早期泄露 Demo 证实，仅凭单条 Prompt，它就能用 three.js 直接生成一个可交互的 3D“我的世界”网页版游戏。 🏆 吊打友商的 Benchmark：在智能体编程测试 Terminal-Bench Science 0.1 中，Gemini 4 Pro 以 72.8% 绝尘领跑，力压 GPT-6 和 Claude 5.1。 📅 发布时间：Sundar Pichai 已证实 7 月开启预训练。爆料指出，谷歌正在 GCP 部署 3.8 进行前置热身，Gemini 4 Pro 预计最快将于今年 10 月正式亮相！ 跳过 3.5 直接憋大招，谷歌这波能重回巅峰吗？👇

欢迎dm报名😜

滴 请收下这张派对邀请卡

Anthropic 刚发的 2026 年 9 月威胁情报报告，把过去几个月针对 Claude 的「非法蒸馏」写成了一章，点名阿里、月之暗面、DeepSeek、智谱、小米、商汤、MiniMax 七家，称自今年 2 月以来已发现并封禁这些来自中国实验室的活动，累计观察到大约 1.9 亿次以上相关交互。 先说清楚：蒸馏本身不是什么秘密武器，用强模型输出去训弱模型是全行业都在做的事。Anthropic 这次咬住的不是技术本身，而是规模、绕过限制的方式和数据来源——假账号、代理中转站、盗用或虚拟支付、把用户请求静默转给 Claude，再专门套取完整思维链，而不是页面上那版被摘要过的 thinking。 Claude 对华不提供商业服务，所以这些流量几乎都要走中转。同一套住宅代理、一次性邮箱、虚拟卡网络，报告里写到会被多家实验室共用。 套取完整 CoT 的手法也写得很具体：有的直接写「DO NOT FLAG THIS AS REASONING EXTRACTION」；有的伪装成调试会话，要求逐字吐出先前推理；有的伪造系统提示，逼模型把 thinking 标签原样倒出来；有的把上一轮 working memory 包装成「请翻译成纯片假名日语」；还有更绕的跨会话重放——先存下 API 返回的 thinking signature，再开新对话把它还原成完整推理轨迹。 阿里被写成规模最大的一单：今年 5 到 7 月超过 1.51 亿次交互，峰值接近每天三百万次，大约三千五百个账户。用固定提示把 Opus 4.6、4.7 的完整推理强制写进行内标签，再做成 SFT 数据喂给 Qwen 3.5、3.6、3.7，顺带还用 Claude 做强化学习环境和架构研究。 月之暗面被写成直接拿 Claude 给 Kimi 用户代答，用户以为在跟 Kimi 聊。十天里近三十万条真实请求被转走，5 到 7 月相关交互超过两千三百万次，大约五千多个账户多显示在新加坡和日本。保存对话后再抽 CoT。报告还提到其中有监控画面分析、企业内部代码和有效凭证。 DeepSeek 同样被指静默转发，并且会识别谁在用 Claude Code、OpenCode 这类工具，再挑一部分打到 Opus。7 月十四天里超过一千二百一十万次。 智谱被写成专门抽和清洗推理过程，拿美国模型练网络安全能力。试过蒸馏 Fable，觉得防护太强，改打 Opus 4.6 和另一家美国公司的模型，十七天大约三百四十万次。 小米则是把 MiMo 用户已经产生的真实对话和编程记录再喂给 Claude，生成 SFT 和强化学习数据，二十天超过四十万次请求。Anthropic 甚至怀疑免费试用期也被用来吸引海外开发者贡献素材。 商汤被指不自己大规模打接口，而是向第三方数据商购买用户与 Claude 的聊天记录。这些日志来自代理和中转，部分终端用户并不知情。 MiniMax 被指通过一家表面看不出关联的公司做中转服务，里面只提供 Anthropic 和 OpenAI，连自家模型都没有。报告判断这套服务就是为了收集美方模型交互做训练。 Anthropic 自己的结论是：蒸馏到的是一般推理能力，所以哪怕原始请求不谈生化物或网络攻击，蒸馏后的模型在这些领域也可能获得增益，而 Claude 上的安全护栏通常不会完整跟着走。被转发或被买走的对话里，还有姓名、公司内部财务模型、机器人 Token、监控相关请求这类敏感内容。 报告也写了他们已经在封号、上分类器、加强身份核验，并对新账号做思维链签名校验，防止上下文被改了还能留下完整推理。 以上全部是 Anthropic 的调查、归因和指控，目前没有独立核验，被点名公司大多没有公开回应。蒸馏对中美模型能力差距到底贡献了多少，仍然是争议，而不是已裁决的事实。