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The TechBeat: Muse, Instinct, Wajo, and OpenAI's Dots: Four AI Assistants That Do the Work for You (11/1/2026)
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## Muse, Instinct, Wajo, and OpenAI's Dots: Four AI Assistants That Do the Work for You
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消息称英伟达RTX 5080 24GB版最快12月上市 或接替中国市场现有高端型号
[分享创造] 挂着日本节点,网站一眼就看出我是中国人。所以我写了个工具, 看谁卖了我
前阵子我一直以为自己藏得挺好。
日本节点,IP 查出来是东京,Amazon 机房。我心想这下网站总认不出我了吧。
👉 https://who.1ms.dog
后来写了个小页面,把网站不用问你、打开就能读到的东西全读了一遍,再拼成一段话。读出来是这样的:
你可能是中国人,现在在东京。你说中文(中国),时区是中国标准时间……DNS 解析器在中国。
IP 是换了,可时区没换,浏览器语言没换,DNS 也还在国内。最离谱的是 WebRTC:它直接把我家联通的 IPv6 报了出来。代理开了个寂寞。

不弹任何授权,它能读到多少
页面里一共 71 个「嗅探器」,全是普通网页就能调的接口:不要定位,不要摄像头,一个授权框都不弹。
读到的东西大概有这些:
- 服务器那边:IP 、城市、ASN 、离你最近的 Cloudflare 节点、TLS 指纹,还有你电脑的时钟准不准
- 浏览器这边:系统和版本、屏幕和刷新率、显卡型号( WebGL 直接报出 RTX 4080 SUPER )、CPU 线程数、内存、键盘布局
- 偏好:深色模式、有没有装广告拦截,甚至装了哪些插件(钱包、沉浸式翻译、Dark Reader 这些会留痕迹)
- 字体:装了哪些字体,能看出你大概是写代码的还是天天用 Office
最后拼成一段话。每个空都能点,点了会告诉你是哪几个嗅探器填的。
年龄那一栏是空的,写着「网站从这些猜不出」。确实猜不出,我也不想瞎编。

我最想要的部分:哪里露了馅
光列数据没意思。网站真正用来识破代理的,是互相对不上的地方。所以右边有 9 项伪装检测:
- 时区和 IP 对不上( IP 在日本,系统是 UTC+8 )
- 浏览器语言和 IP 所在国家对不上
- DNS 泄露:替你解析域名的 DNS 在国内
- 分流:国内网站、国外网站、ChatGPT 、Claude 看到的出口不一样
- WebRTC 泄露真实地址
- IP 是机房的(很多网站直接拿这个判断你在挂代理)
- UA 说自己是 Mac ,显卡却是 NVIDIA
- 指纹稳定不变,网站能一直认出你
navigator.webdriver为真,一看就是脚本在控制浏览器
每一项标红标黄的,旁边都有个「怎么修」,展开就是具体操作:DNS 泄露怎么配 fake-ip 或 TUN ,WebRTC 怎么关,时区去哪改。
我自己照着改完,从 7 处降到了 2 处。剩下那两处是机房 IP 和指纹,前一个是节点的问题,后一个本来就没什么好办法。
WebRTC 泄露单独做了一页
这个坑踩的人太多了,所以单独拉了一页:who.1ms.dog/zh-cn/webrtc-leak/
它会把 WebRTC 拿到的地址和网页看到的地址放在一起对。IPv4 、IPv6 分开比,只有确认是另一个出口才报红。有些 VPN 只转发 IPv4 ,漏出来的 IPv6 也会给你标出来。

浏览器指纹这件事
页面上有张「指纹卡」,是用 Canvas 、WebGL 、音频、字体算出来的一串 ID 。
换网站、开无痕、开 VPN ,这串东西都不变。下次再来,它会告诉你「上次是哪天来的,指纹变没变」。这个记录只存在你自己浏览器里,我这边拿不到。
但它说明了一件事:网站不用 Cookie 也能认出你,平时那么多「跨站追踪」,靠的就是这个。
可以分享,但不带 IP
测完可以生成一张图或一个链接发给朋友。

分享出去的内容里没有 IP ,也没有指纹。城市和运营商默认打码,要自己勾选才会带上。朋友打开链接,看到的是你的画像,下面有个按钮「测测我自己」。
关于隐私
做这种东西,自己先得干净:
- 后端只有一个接口,返回它从这次请求里本来就能看到的东西( IP 、请求头、Cloudflare 给的地理位置),不存、不记日志
- 除此之外全在浏览器里算
- 为了查出口和 DNS ,页面会直接请求几个外部服务( IPIP 、IP.SB 、chatgpt.com 、claude.ai 等),每一行都写了是谁,你访问它们时它们本来就能看到你的 IP
- 截图前可以点「隐藏位置」,地址和城市会模糊掉,我这几张图就是这么截的
开源,MIT
https://github.com/yt1988/who-are-you
跑在 Cloudflare Workers 上,npx wrangler deploy 就能部署一份自己的。支持 10 种语言。
最后,回帖说说你挂着代理能暴露几处?我改之前是 7 处,现在 2 处,机房 IP 实在没办法 😂
李飞飞挖的三个「坑」:从 ImageNet 到空间智能,这一次她会怎么填?
82 亿美元,已经为整个世界模型赛道提供了价值锚点。 作者丨幸丽娟
编辑丨岑 峰
82亿美元,全股票收购李飞飞创立的 World Labs。这是 AMD 史上第二大收购,仅次于 2022 年以约 500 亿美元拿下赛灵思。赛灵思是30多年的老牌芯片公司,而 World Labs,仅仅成立两年。这一消息发出后,圈内人士的第一反应很一致:苏姿丰在对标英伟达的 Cosmos 和机器人平台,买人、买模型洞察力、买一张通往物理AI时代的入场券。但苏姿丰不是这笔交易里唯一在下注的人。李飞飞也在下注。而且她押上的,是自己的下一个二十年。过去二十年,她反复在做同一件事:在每一个技术拐点,先一步定义“什么是值得做的下一件事”。但前两次,她只负责出题,把答题的活儿交给别人。而这一次,她把自己按在了答题席上。

01
三次“挖坑”,有何不同?
被誉为“AI 教母”的李飞飞,圈内人对她印象最深的,或许是她最擅长“挖坑”(出题)。过去二十年,她挖的三个最关键的坑:ImageNet、AI 民主化、空间智能。单独看每一个,都是对 AI 行业走向的一次精准判断。但摆在一起看,她扮演的角色截然不同。▎第一个“坑”:纯出题,整个深度学习社区答题
2007年前后,李飞飞在普林斯顿做助理教授。计算机视觉领域当时最要命的问题是泛化——算法在实验室里跑得通,出了门就失灵。大多数人选择改进算法,李飞飞选了另一条路:造数据。这个选择在当时的学术圈几乎没人看好。普林斯顿的资深教授劝她别干,说“太有野心了”;申请联邦经费被反复拒绝,甚至有人批评这个课题是“普林斯顿之耻”。不仅如此,她还赌上了自己的学术前途。如果ImageNet失败,她很可能拿不到终身教职。
这个数据集不解决任何算法问题。它只提出了一个问题:我们用什么标准衡量视觉智能?这个问题在当时切中要害。没有足够的数据,一切算法创新都是“沙上筑塔”。
Pika 基于 MiniMax H3 开源 H3 Flex,六步实现音视频联合生成
近日,Pika 发布 MiniMax H3 衍生模型 Pika H3 Flex,并开放适配器权重与推理代码。该模型通过六步去噪联合生成画面和声音,支持文本、首尾帧和多模态参考输入,供创作者进行镜头设计、参考生成和视频编辑。

本次发布包含两套 LoRA 适配器,分别用于文本及首尾帧条件生成、多模态参考生成,同时提供 MiniMax H3 基础组件和本地推理工具。模型原生支持 480P、768P 输出,可生成约 5 至 15 秒的视频及同步立体声音频。Pika 提供 ComfyUI 工作流和 Hugging Face Diffusers 接入方式,方便开发者将其加入现有创作工具。
在本地生成之外,Pika 可通过 API 提供 1080P、2K 和 4K 视频增强,以及音频分轨功能。创作者可以先以较低分辨率生成和筛选草稿,再对选定版本进行高分辨率处理;同步生成的声音则可拆分为对白、配乐、音效、拟音和环境声五类独立音轨,方便在后期分别调整。高分辨率的增强视频和音频分轨则由 Pika API 服务提供。
Pika 在技术博客中介绍,团队先在内部音视频数据集上对 MiniMax H3 进行监督微调,再采用投影分布匹配蒸馏(PDMD),将教师模型的 50 步去噪压缩至 6 步。随后,团队通过奖励反馈学习和直接偏好优化训练 LoRA 适配器,改善美学质量、清晰度和指令遵循。适配器合并用于推理后,无须增加生成步数。
Pika 还对托管推理服务做了加速。在保持模型权重和六步采样方案不变的测试中,使用 8 张 NVIDIA B300 GPU 生成一段 5 秒、768P 音视频,条件编码、去噪和解码的合计耗时从 3.409 秒缩短至 2.129 秒,处理速度约为原来的 1.6 倍。

在视觉与音频表现上,Pika 公布的对比评测结果显示,H3 Flex 在五项视觉和音频质量指标上均在参评模型中取得最高得分,并在 DesignArena 文生视频榜单上取得第八名成绩,是该榜单下排名最高的开源视频模型。
今年 8 月,MiniMax H3 开源后,MiniMax 与合作企业围绕不同的使用场景,继续训练和优化模型,并在社区中开放,包括与 Running Hub 打造 MiniMax H3 480P;与 fal 选择面向实时生成做后训练和推理优化,推出了 H3 Max 768P、H3 Max 480P;以及 FastVideo 通过稀疏化和硬件适配推出的 FastH3、英伟达与 MIT 联合围绕 H3 的开发 StreamRender-H3、Open-H3-Regenerate-2K 等多款模型等,海外开发者围绕 H3衍生模型搭出了 Twitch 直播、24 小时“AI 电视台”和 AI 短视频直播软件等创新玩法。
H3 开源一个月内下载量已经超过 2400 万次,产生超过 300 个公开衍生模型,成为 2026 年全球下载量最高的模型之一。
苹果收盘市值5.03万亿美元反超英伟达,重夺全球第一
5 Key Reasons AV1 Will Play a Big Role in WebRTC Streaming
In this blog, you will learn why AV1 is getting closer to practical use in WebRTC-based real-time interactive streaming. We cover rapid progress in live AV1 encoding on GPUs and ASICs, widening browser and device support, and new WebRTC compatibility work like SVC and RTP payload formats. You will also see how Red5 Pro’s XDN and LCEVC can lower compute costs and expand device reach as AV1 adoption accelerates.
Advances supporting the next-generation AV1 codec have intensified over the past year, adding new urgency to the question of when the technology might become useful in WebRTC-based real-time interactive streaming applications.
There’s no question live video encoding supported by AV1 would benefit real-time streaming every bit as much as it stands to benefit traditional HTTP streaming. The codec achieves bitrate reductions in the range of 30%-40% compared to HEVC/H.265 and 60%-70% relative to AVC/H.264. Moreover, AV1 is available royalty-free with major tech sector backing through the Alliance for Open Media (AOMedia), whose members include Amazon, AMD, Apple, Cisco, Google, Hulu, IBM, LG, Meta, Microsoft, Netflix, Nvidia, Intel, IBM, Samsung, and many other leading players.
But the amount of computing power it takes to run the encoding and decoding processes has been a major impediment to AV1 adoption, especially when it comes to encoding at live video frame rates. As demonstrated in countless tests, there’s been no practical way to use AV1 to encode video in real time except at very low levels of resolution. For example, a series of recent tests conducted by cloud CDN and compute platform operator Cloudflare found that even if all 48 cores of the top-line AMD EPYC 7642 CPU processor were used to encode a 4K clip commonly used in such testing, it would take 43 seconds to encode two seconds.
But the perspective on the possibilities is shifting rapidly. And we at Red5 Pro are paying close attention to developments.
1. AV1 Live Encoding Is Closer than Ever to Commercial Viability
In 2021, Google released its desktop Chrome 90 browser with an AV1 encoder optimized for video conferencing with WebRTC. The encoder is targeted to two- to four-core CPUs for 2 Mbps transmission of 720p video at 30fps. This points to the fact that encoding AV1 for low bit-rate transmissions at sub-1080p levels is now doable on most PCs, which means the codec can be widely used to get decent-quality video over low-bandwidth mobile links that are the sole means of internet access in many parts of the world.
Cisco took another step in this direction in 2021 on the fringes of its Webex video conferencing service. The company now gives users the option to turn on live AV1 encoding to improve the quality of shared displays of video clips during conference calls.
Anyone on such a call with a device capable of decoding a low-bitrate AV1 stream will see the clip with much greater clarity. Cisco said it expects to expand the use of AV1 on Webex over time but had not announced any further steps as of YE 2022.
A much more dramatic sign of progress on the AV1 encoding front came in late 2022 with the introduction of powerful new AV1-enabled graphics engines by Intel, AMD, and Nvidia. As of this writing, for the first time, live AV1 encoding in fast-action gameplay can be executed by some versions of Intel’s Arc Alchemist and Nvidia’s RTX 4000 series running on gaming cards, which became commercially available in October. AMD released its far less expensive RX 7000 platform in early 2023 with no lofty claims for live AV1 encoding. Even so, early comparisons of the three tech companies’ latest advances, like this one reported by PC World, found them more or less on par with one another when it came to AV1 encoding of content for recording and archival usage.
It’s clear the AV1 live streaming barrier has been broken for gamers who want to shell out $1,500 or so to purchase the Intel or Nvidia gaming cards now on sale from multiple vendors. For example, testing of live video encoding with Intel Arc’s AV1 encoder against leading H.264 GPU encoders, as reported by PC World, found that when set to encode HD 1080p video at 3.5 Mbps, the quality of the video is superior to the output of H.264 encoders delivering the content at nearly twice that bitrate.
Of course, GPU cards significantly outperform encoders running on CPU cores or CPU cores enhanced with GPU acceleration, which remain the primary encoding engines in OTT streaming services. But these latest GPU platform advances point to the likelihood that the densification and architectural advances achieved with gaming encoders will be adopted in succeeding generations of hybrid CPU/GPU processors.
Another hardware advance signaling a role for live AV1 encoding occurred at the 2022 IBC trade show in Amsterdam, where RealSprint, the Swedish company operating the Vindral CDN service, revealed it would be introducing AV1 live encoding on its platform using ASIC (application-specific integrated circuit) hardware technology from NETINT Technologies. NETINT’s low-latency 4K AV1 encoder harbingers the emergence of ASICs as a potential game changer in advanced codec operations, according to NETINT COO Alex Liu, as quoted by Streaming Media. “[These applications] can only be met with ASIC hardware encoding,” Liu said.
2. Browser, Player Support for AV1 Decoding Is Gaining Wider Traction
Meanwhile, on the decoding side, there’s been a surge in browser and app support for AV1 software decoding on high-power devices. For now, this means that entities streaming on-demand content can use AV1 encoding with high confidence that a growing share of users will be able to take advantage of the bandwidth savings, which are beneficial to distributors as well as receivers.
The Google-backed Chrome browser and apps based on Android 10 or later can support software-based AV1 decoding on any device with enough processing power to accommodate the codec. Firefox, too
Greece's AI Strategy: How DAEDALUS and PHAROS Could Shape Its Future
DAEDALUS's inauguration in Lavrion on 19 October 2026 brings Greece's immediate AI policy question into sharper focus: how will institutions use the new computing base? EuroHPC and GRNET point to a delivered supercomputing installation, following earlier procurement and benchmark milestones. [1] This creates an opportunity for researchers, businesses and public organisations to attempt work that previously required different access arrangements. Its strategic significance will emerge through the projects that can use it reliably and the institutions able to retain the resulting expertise.
The most plausible Greek role is selective. A country does not need to lead every part of the AI system to build useful capabilities. It does need to choose applications where computing resources, relevant data and responsible organisations can be brought together. For Greece, PHAROS identifies health, culture and language, and sustainability as priority domains. [2] These provide a focus for assessing outcomes rather than treating every new AI activity as evidence of national progress.
Establishing what has been delivered
The DAEDALUS timeline deserves precision. EuroHPC launched the tender in June 2024. EuroHPC announced the signed contract with Hewlett Packard Enterprise on 28 March 2025. In June 2026, the system appeared in the TOP500 ranking, while EuroHPC still described wider European availability as forthcoming. The October inauguration is a further milestone. [3–5, 1] These events establish different things: procurement, contractual commitment, benchmark performance and public inauguration should remain distinct in the record.
At inauguration, EuroHPC reported sustained performance of 85.69 petaflops and peak performance of 115 petaflops. The system uses HPE infrastructure and more than 2,000 NVIDIA GH200 Grace Hopper Superchips. [1] Neither performance figure measures the quality of an AI model or the effectiveness of a public service. Benchmark capacity becomes useful to an organisation only when it has access, suitable software and people able to formulate and execute the work.
Hosting, access and responsibility
DAEDALUS is operated by GRNET within an international arrangement. The procurement documentation states that access is jointly managed by EuroHPC and the DAEDALUS consortium in proportion to investment. The acquisition cost was €36 million, with 35% funded by EuroHPC and 65% through the EU-financed Greece 2.0 recovery plan; Cyprus, Montenegro and North Macedonia also participate in the consortium. [1, 4]
This arrangement broadens the resources and relationships available to Greece. It also means that the machine's location does not give every Greek institution unrestricted or immediate access. Ministries, universities and firms need to understand the relevant allocation routes, eligibility conditions and support arrangements. A national strategy should therefore examine who can obtain useful computing time, how long applications take and whether users can continue their work after an initial project.
External technological dependence also remains material. Hardware supplied by HPE and NVIDIA brings substantial capabilities, while tying parts of the system's operation and renewal to those suppliers. [1] The policy response should concern support continuity, staff competence and credible options for future workloads. Owning or hosting infrastructure cannot by itself remove dependence on the organisations that design and maintain essential components.
PHAROS as the connection to use
The distinction between Invention, Industrialisation and Operationalisation helps locate the task. Invention includes the development of methods, models and specialised knowledge. Industrialisation supplies the infrastructure and repeatable technical processes needed to work at scale. Operationalisation concerns the sustained use of those capabilities in organisations. Governance connects the layers by determining access, data permissions, funding and accountability.
PHAROS describes its purpose as connecting academia, industry and the public sector through computing infrastructure, services and expertise. [2] Its importance lies in that organisational connection. A research group may need help adapting software; a small firm may need technical guidance; a public agency may need a clear procedure for using data and evaluating results. These requirements differ, even when users share the same machine.
For Greek-language applications, a plausible objective is better performance on particular public or professional tasks, supported by transparent evaluation. For environmental applications, it could be tools that improve a defined monitoring or planning process. In health, an initial priority could be carefully governed research workflows. These are proposed directions, not claims that PHAROS has already delivered those outcomes. Each requires an accountable institution and a plan for continuing the service after development funding ends.
How to judge the next phase
Four indicators would make progress observable. First, published access and utilisation records should show which categories of users obtain computing time and whether access meets their needs. Second, completed projects should report task-specific results, including failures and costs. Third, repeated use by public bodies and firms should reveal whether initial demonstrations become sustained activity. Fourth, staff retention and maintained software should show whether technical knowledge remains available within the organisations using it.
These indicators would also help direct resources. Heavy demand without sufficient support would suggest an implementation bottleneck. Available capacity with few viable projects would point towards weaknesses in project preparation, data access or institutional demand. The appropriate response would differ in each case. Aggregate computing performance cannot diagnose these problems.
Greece can develop a meaningful role as a location for selected AI applications and regional collaboration if it converts the new infrastructure into dependable institutional capability. DAEDALUS provides a stronger starting point, and PHAROS offers a route for organising its use. The decisive evidence will be organisations that can solve relevant problems, maintain their systems and exercise responsibility for the consequences.
How to read this profile
This is original country analysis applying the GOAI capability conversion framework. It is not a peer-reviewed country assessment or a scored ranking. Proposed applications and indicators are analytical judgments; company and government claims are attributed to their sources.
Sources
All sources were accessed on 20 October 2026. Future applications and project objectives remain distinct from delivered infrastructure.
- EuroHPC JU. DAEDALUS inauguration and system specifications. 19 October 2026.
- PHAROS AI Factory. Project mission and priority domains. Undated current project page.
- EuroHPC JU. DAEDALUS tender announcement. 25 June 2024.
- EuroHPC JU.
The AI-Bubble Scorecard
As of October 23rd, 2026
"AI bubble" had one of its loudest weeks of the year. CoreWeave slid to a new 52-week low on financing worries, Nvidia fell 4.4% on October 22, and Ken Griffin told a Citadel conference investors are "almost certainly" in a bubble, which promptly drew pushback from Deutsche Bank's Christian Sewing and BlackRock's Larry Fink. Jensen Huang, meanwhile, said neocloud demand is off the charts, but capital for them is scarce.
I kept a score of all the pieces I saw this week, so 21 articles, reports, and columns from October 16 to 23 that came up in my searches with enough text to judge. Each piece is scored on its overall verdict: Bubble if it says AI is a bubble and/or about to pop; Not a bubble if it rejects the label or says burst fears are premature; Mixed if it presents both sides or says a bubble exists but isn't about to burst.
Result: 8 bubble · 4 not a bubble · 9 mixed (21 pieces)
Oct 16, Open Markets Institute: report on circular financing says the AI bubble is "primed to pop." Bubble
Oct 16, IoT Analytics: analysis concluding there's no widespread AI bubble. Not a bubble
Oct 17, Fortune: Wall Street debates whether the warnings are a head fake. Mixed
Oct 17, The American Bazaar: the bubble "isn't bursting, it's being audited." Mixed
Oct 18, Bloomberg: billionaires warn of a bubble, Howard Marks unconvinced. Mixed
Oct 19, CEPR (Dean Baker): AI Bubble Monitor #19 Bubble
Oct 20, NPR / WBUR: Is the AI boom a bubble waiting to burst? Mixed
Oct 20, Bloomberg: Griffin says AI is "almost certainly" a bubble. Bubble
Oct 21, 24/7 Wall St. (via Yahoo Finance): Griffin's bubble remarks. Bubble
Oct 21, Bloomberg: Federated Hermes CIO says the boom has years to run. Not a bubble
Oct 21, Bloomberg: Sewing and Fink push back on Griffin. Not a bubble
Oct 21, Axios: The AI trade gets a reappraisal. Mixed
Oct 21, Common Dreams: report warns the bubble is "primed to pop." Bubble
Oct 21, The Blaze: Ed Zitron says the bubble is about to pop. Bubble
Oct 21, Investing.com: Yardeni says a bubble exists but isn't about to burst. Mixed
Oct 21, Investing.com: "AI bubble or not," manage the risk. Mixed
Oct 21, Around Prague: a 16% weekly drop is a correction, not a collapse. Not a bubble
Oct 22, CounterPunch: is CoreWeave's bad week the beginning of the end? Bubble
Oct 22, Luminix: the evidence for and against. Mixed
Oct 23, Asia Times: report says the bubble is "primed to pop." Bubble
Oct 23, Tech Diplomacy (Substack): boom-or-bubble roundup. Mixed
Reading the numbers: The bubble narratives are more concentrated than it looks. Three of the eight entries cover the same Open Markets/Americans for Financial Reform report, and two cover the same Griffin remarks, so by distinct underlying argument it's closer to 5 bubble vs 4 not.
The bigger tell is the mixed column: the most common position this week was some version of Yardeni's line, that the conditions for a bubble are there (its new meter reads 83 out of 100) but the triggers that ended past booms are only partly present (34 out of 100). Almost nobody in the sample argued AI itself is overhyped as a technology; the fight is about debt, financing, and whether the spending pays back in time.
Sources
Bloomberg (Oct 18, 20, 21), Fortune (Oct 17), Axios (Oct 21), NPR/WBUR (Oct 20), CEPR AI Bubble Monitor (Oct 19), Common Dreams (Oct 21), Asia Times (Oct 23), CounterPunch (Oct 22), The Blaze (Oct 21), 24/7 Wall St. (Oct 21), Investing.com (Yardeni; "AI Bubble or Not"), Around Prague, IoT Analytics, The American Bazaar (Oct 17), Luminix, Tech Diplomacy Substack (Oct 23), CNBC (Oct 22, Huang), The Motley Fool (Oct 22, CoreWeave/Nvidia closes)