索尼A7R VI发布
近日，索尼正式推出新一代全画幅微单相机A7R VI，标志着高分辨率摄影技术迈上新台阶。过去，使用超高像素相机往往意味着在画质与便携性、处理速度之间做出妥协，而A7R VI则致力于打破这一瓶颈。该机型在保持约6100万像素高分辨率的基础上，大幅提升了连拍速度、自动对焦性能及动态范围表现，并引入了更先进的图像稳定系统，让摄影师在手持拍摄时也能获得清晰锐利的画面。此外，A7R VI还优化了视频拍摄能力，支持8K录制，满足专业创作者对静态与动态影像的双重需求。分析人士指出，随着计算摄影和AI辅助技术的普及，传统相机厂商正通过硬件创新维持竞争力，而A7R VI的发布或进一步加剧高端相机市场的竞争格局。 #索尼 #A7RVI #相机 #摄影 #科技新闻 #高像素
近日，索尼正式推出新一代全画幅微单相机A7R VI，标志着高分辨率摄影技术迈上新台阶。过去，使用超高像素相机往往意味着在画质与便携性、处理速度之间做出妥协，而A7R VI则致力于打破这一瓶颈。该机型在保持约6100万像素高分辨率的基础上，大幅提升了连拍速度、自动对焦性能及动态范围表现，并引入了更先进的图像稳定系统，让摄影师在手持拍摄时也能获得清晰锐利的画面。此外，A7R VI还优化了视频拍摄能力，支持8K录制，满足专业创作者对静态与动态影像的双重需求。分析人士指出，随着计算摄影和AI辅助技术的普及，传统相机厂商正通过硬件创新维持竞争力，而A7R VI的发布或进一步加剧高端相机市场的竞争格局。 #索尼 #A7RVI #相机 #摄影 #科技新闻 #高像素
Agent-shell 0.73 更新
agent-shell 是一款基于 Emacs 的原生模式，用于与遵循代理客户端协议的 AI 代理交互。最新 0.73 版本新增了聊天模式，融合 comint 模式与传统聊天标签体验，现已默认启用，用户可通过设置关闭。更新还优化了活动分组功能，提供默认折叠、展开或仅展开最新活动的选项。提示队列功能得到改进，用户可在代理忙碌时排队提示词，并支持查看、恢复或移除待处理项。新增的 compose 功能允许从任意缓冲区起草提示词，支持快速发送和连续创建。初始化速度加快，用户无需等待即可输入。此外，Markdown 列表渲染得到优化，TAB 导航功能也有改进。该工具自去年九月推出以来，持续获得功能增强。 #Emacs #AI代理 #agent-shell #开源软件 #编程工具
agent-shell 是一款基于 Emacs 的原生模式，用于与遵循代理客户端协议的 AI 代理交互。最新 0.73 版本新增了聊天模式，融合 comint 模式与传统聊天标签体验，现已默认启用，用户可通过设置关闭。更新还优化了活动分组功能，提供默认折叠、展开或仅展开最新活动的选项。提示队列功能得到改进，用户可在代理忙碌时排队提示词，并支持查看、恢复或移除待处理项。新增的 compose 功能允许从任意缓冲区起草提示词，支持快速发送和连续创建。初始化速度加快，用户无需等待即可输入。此外，Markdown 列表渲染得到优化，TAB 导航功能也有改进。该工具自去年九月推出以来，持续获得功能增强。 #Emacs #AI代理 #agent-shell #开源软件 #编程工具
OpenAI 等 AI 巨头实习生日薪曝光
据 Odaily 报道，全球 AI 巨头实习生薪酬呈现显著分层。顶尖项目中，OpenAI Residency 项目月薪达 1.8 万美元（日薪约 5625 元），面向核心研究团队，门槛极高，被视为顶尖研究员预招聘；Anthropic 的 AI 安全研究员周薪 3850 美元（日薪约 5198 元），另配每月 1.5 万美元算力经费，首期学员超八成有论文发表。美国常规技术实习岗中，Meta 日薪约 3780 元，Google 约 3400 元，英伟达博士岗上限可超 5000 元。相比之下，中国顶尖专项计划如字节跳动 Top Seed 日薪 2000 元，小米顶尖岗日薪 1000-1100 元，而普通岗如 DeepSeek 日薪 500-1000 元，阿里 350-550 元，智谱 200-300 元，部分公司以高比例员工持股作为长期补偿。网传 DeepSeek 实习生日薪 5500 元未经官方确认，属顶尖个例，中美薪酬差距仍达 6-8 倍。 #OpenAI #AI #实习 #薪酬 #科技 #人才竞争 #Anthropic #DeepSeek #字节跳动 #大模型
据 Odaily 报道，全球 AI 巨头实习生薪酬呈现显著分层。顶尖项目中，OpenAI Residency 项目月薪达 1.8 万美元（日薪约 5625 元），面向核心研究团队，门槛极高，被视为顶尖研究员预招聘；Anthropic 的 AI 安全研究员周薪 3850 美元（日薪约 5198 元），另配每月 1.5 万美元算力经费，首期学员超八成有论文发表。美国常规技术实习岗中，Meta 日薪约 3780 元，Google 约 3400 元，英伟达博士岗上限可超 5000 元。相比之下，中国顶尖专项计划如字节跳动 Top Seed 日薪 2000 元，小米顶尖岗日薪 1000-1100 元，而普通岗如 DeepSeek 日薪 500-1000 元，阿里 350-550 元，智谱 200-300 元，部分公司以高比例员工持股作为长期补偿。网传 DeepSeek 实习生日薪 5500 元未经官方确认，属顶尖个例，中美薪酬差距仍达 6-8 倍。 #OpenAI #AI #实习 #薪酬 #科技 #人才竞争 #Anthropic #DeepSeek #字节跳动 #大模型
Paper on Architecture for AI-Assisted Software Development
Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hartwig Grabowski [ view email ] [v1] Thu, 25 Jun 2026 13:51:22 UTC (21 KB) Full-text links: Access Paper: View a PDF of the paper titled The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development, by Hartwig Grabowski View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected
Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hartwig Grabowski [ view email ] [v1] Thu, 25 Jun 2026 13:51:22 UTC (21 KB) Full-text links: Access Paper: View a PDF of the paper titled The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development, by Hartwig Grabowski View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected
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U.S. to tell partners they must pick sides in AI race with China
WASHINGTON – The U.S. is preparing to tell dozens of countries they must pick sides in the artificial intelligence race with China, warning they will be excluded from a U.S.-led AI coalition if they also sign up for Beijing’s competing framework, according to a U.S. official and an internal draft reviewed by reporters. Washington last year launched the Pax Silica initiative aimed at securing supply chains for AI models, semiconductors and critical minerals, amid a fierce technology rivalry with Beijing. About two dozen countries have joined, including Kazakhstan, a key potential source of critical minerals that has also joined China’s coalition, as well as close U.S. allies such as Japan, Australia and South Korea.
WASHINGTON – The U.S. is preparing to tell dozens of countries they must pick sides in the artificial intelligence race with China, warning they will be excluded from a U.S.-led AI coalition if they also sign up for Beijing’s competing framework, according to a U.S. official and an internal draft reviewed by reporters. Washington last year launched the Pax Silica initiative aimed at securing supply chains for AI models, semiconductors and critical minerals, amid a fierce technology rivalry with Beijing. About two dozen countries have joined, including Kazakhstan, a key potential source of critical minerals that has also joined China’s coalition, as well as close U.S. allies such as Japan, Australia and South Korea.
The AI Situation in Software Development
Random thoughts about prompting, context windows, compression, and working with AI. You want it to do something you have in mind, and you know how/what to do, or sometimes you don’t. Now there are 3 options: you tell it everything down to the details, every specific thing. Or you just tell it to do something at a high level and expect the thing to understand. Or you can go the middle way. I feel this is the go-to way, explaining the important parts that you think might be difficult for it. You can feed it examples. It’s a faster way to do things, but it depends on the example being close to what you want. All of these are time-consuming. Some you spend time before giving to AI, some after. A common pattern is easy for LLMs to implement, considering they must have seen it before in their training set, for example implementing user auth. A n
Random thoughts about prompting, context windows, compression, and working with AI. You want it to do something you have in mind, and you know how/what to do, or sometimes you don’t. Now there are 3 options: you tell it everything down to the details, every specific thing. Or you just tell it to do something at a high level and expect the thing to understand. Or you can go the middle way. I feel this is the go-to way, explaining the important parts that you think might be difficult for it. You can feed it examples. It’s a faster way to do things, but it depends on the example being close to what you want. All of these are time-consuming. Some you spend time before giving to AI, some after. A common pattern is easy for LLMs to implement, considering they must have seen it before in their training set, for example implementing user auth. A n
ew problem you’re imagining or telling it is of course hard for it and needs hand-holding. Then there’s the context window problem. You can’t just give a 3000-word, 4-page detailed dense spec and expect it to follow everything, and the larger the codebase, the less it can pack everything in, nor are the vast documents you can feed it worthwhile. Not only for writing detailed specs ~ you also want it to summarize patterns, draw conclusions from a large dataset, be it something like analyzing vast amounts of numerical data, for example a historical dataset for a stock. So the cost is on you: you still need to spend the time to write a detailed guide for your project, its goals and its issues, and more importantly the blueprint of the thing you want. Then there’s domain-specific expertise of AI models. You need to pick and choose the right one. As you work on bigger problems and as you integrate AI into your applications, a need for compression arises, packing as much useful information as possible, if not all, into your AI agent to solve a particular problem or to draw a conclusion, make a decision or whatever. I think there will be companies in this space that’ll do this effectively, or the model builders will just solve this once and for all. There must be feedback loops in terms of tests, tooling (purpose-built or otherwise), and refining its approach as the codebase grows large. And ways for improving the signal-to-noise ratio in your codebase. I feel that great explainers or natural teachers find it easy to engage with AI and produce better outputs. Bottom line: you still need to spend time. The implementation time is gone. Now the time you spend has shifted to designing the system upfront, changing assumptions, and refining your dev setup. But implementation is not really gone. I feel I am still implementing in words instead of code.