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Kids love 3D printers almost as much as K-Pop Demon Hunters. Just give in and get one.

Everything is expensive. Treat yourself to one of these WIRED-tested and -approved Prime Day picks under $30.

Environment And Situation Control room of an apartment Number of installed product : 3 (PC-based NVR, dual-LAN supported) Remote support : X (I actually went to the site and diagnosed) Reported Issue In viewer, when user changes play speed while playing back the recorded data, it randomly plays the data in hyper speed(almost 30x~60x) For example: 4x play means 4 seconds in video per a second. But in the site, it played 30~60 seconds per a seconds, showing the video stutturing. Diagnosis Checked the overall environment. System(CPU / RAM usage), network environment(bandwidth), resoulution, stream configurations, etc. -> Nothing suspicious. Some of the installed cameras had unusual fps and gop values Normally, fps and gop values are set to be equal(for exmaple, if fps is 30 then gop is also 30 so that iframe can appear every second) But the cameras' set up values were fps 15, gop 60(iframe per 4 seconds) Assumption Somehow the viewer keeps failing to find iframe to play. And it's maybe because iframe appears with a long gap. Quick note: iframe is kind of a key-frame. Since the viewer starts decoding from an iframe, it's necessary when it comes to playback. What I Tried Set all the cameras' gop value to 15(same as fps) Result Ran a test with data before changing the gop values and after. During interval before changing the gop, the issue occurred almost every time I tried. But after chaning the gop, the issue no longer occurred. Concolusion The issue was triggered by large GOP value (GOP 60 with FPS 15). With only one iframe every four seconds, the viewer sometimes failed to find an appropriate iframe after changing the playback speed, causing abnormal playback behavior. According to the viewer developer, this is likely related to the viewer's iframe searching logic, which is still under investigation. Keep This In Mind Check camera settings(especially gop and fps) first when it comes to playback issue. Always check before/after data to confirm assumption.

Explore how the GitHub Copilot agentic harness delivers strong results across multiple benchmarks and leading token efficiency, while maintaining flexibility to choose among more than 20 models. The post Evaluating performance and efficiency of the GitHub Copilot agentic harness across models and tasks appeared first on The GitHub Blog .

Provisioning a Bedrock RAG knowledge base with S3 Vectors, without the hallucinated API calls. If you've asked an AI coding agent to set up AWS, you've seen it confidently invent a parameter, reach for a deprecated service, or burn ten minutes retrying against a service it never saw in training. The failure mode that bites hardest is the silent one: the agent thinks it succeeded, and you find out an hour later. I hit two of these while standing up the retrieval layer for a LangGraph support bot, an Amazon Bedrock Knowledge Base backed by Amazon S3 Vectors. I'd love to say I caught both with deep AWS expertise. I caught them because the Agent Toolkit for AWS read the docs I hadn't. Both would have shipped, and neither did. The 30-second setup The goal: take a folder of markdown product docs and make them queryable by meaning, so an agent can answer "is this safe for color-treated hair?" from the real docs instead of guessing. Think of it as giving the agent a library it can search instead of making things up. That's the retrieval half of RAG, the foundation a LangGraph agent will later call as a tool. Four moving parts, wrapped in one managed service: Source bucket : an S3 bucket holding the docs. Embeddings : Amazon Titan Text Embeddings V2 (1024-dim vectors). Vector store : Amazon S3 Vectors. I chose it over OpenSearch Serverless because it has no always-on compute, the difference between cents and a monthly surprise for a demo that sits idle. Knowledge Base : Amazon Bedrock Knowledge Bases ties it together into one thing you can query with a retrieve call. To follow along, you need an AWS account, a non-root IAM identity with credentials configured locally, uv installed, and the toolkit installed in your agent. The fastest path across Kiro, Claude Code, Cursor, and Codex is the AWS CLI installer, aws configure agent-toolkit ; in Kiro you can instead add the AWS MCP Server to .kiro/settings/mcp.json (pin the mcp-proxy-for-aws version) and run npx skills add aws/age

🌟 开场白：你有没有这样的烦恼？ 小朋友们，有没有遇到过这种情况： 📱 手机里的照片太多， 装不下了 ！ 💻 电脑里的视频， 换了电脑就找不到了 ！ 👨‍👩‍👧 爸爸妈妈爷爷奶奶，想看同一个视频， 要互相发来发去 ！ 有没有一个地方， 所有人都能存东西、随时取东西 ？ 有！那就是 —— 🏠 NAS N etwork A ttached S torage 网络附加存储 （但老师觉得叫它 "家庭小仓库" 更好懂！） 🎒 第一课：NAS到底是个啥？ 先想象一个场景 👇 幼儿园有个 大储物柜 🗄️ 每个小朋友都有 自己的格子 在教室里、在走廊里、甚至在家里 只要知道密码，随时可以取东西！ 老师也可以把作业放进去，大家一起看 这个 "随时随地都能访问的大储物柜" ，就是 NAS ！ 👩‍🏫 老师比喻总结： 普通硬盘 NAS 只插在一台电脑上用 接在路由器上，全家都能用 只有这台电脑能访问 手机、平板、电脑都能访问 出门就用不了 出门在外也能访问 🌍 像你自己的小书包 🎒 像幼儿园的公共储物柜 🗄️ 🏗️ 第二课：NAS长什么样？ NAS其实就是一台 特别的小电脑 🖥️ 普通电脑 = 有屏幕、键盘、鼠标 NAS = 没有屏幕！没有键盘！没有鼠标！ 只有一个"装硬盘的盒子" + 网线插口 ┌─────────────────┐ │ NAS 小盒子 │ │ ┌───┐ ┌───┐ │ │ │硬 │ │硬 │ │ ← 装了好几块硬盘 │ │盘1│ │盘2│ │ │ └───┘ └───┘ │ │ 💡 小灯灯 │ └────────┬────────┘ │ 网线 │ 📡 路由器 / | \ / | \ 手机 电脑 平板 👩‍🏫 老师比喻： NAS = 一个 装了很多大肚子的小机器人 🤖 它不需要眼睛（屏幕）、不需要手（键盘） 它只需要 网线 ，就能默默给全家服务 💪 🤯 第三课：重点来了！ NAS 其实就是一个"网页操作系统"！ 小朋友们先回忆一下上次学的 👇 网页 = HTML骨架 + CSS衣服 + JavaScript动作 然后放在 服务器 上，用 浏览器 访问 现在老师告诉你一个秘密 🤫 NAS 本身就是一台服务器！ 你管理NAS，不需要接显示器 直接打开浏览器，输入地址 NAS就把它的"控制面板"当网页显示给你！ 就像这样 👇 你打开浏览器，输入：http://192.168.1.100 ↓ NAS的"网页控制面板"出现了！ 有文件管理、有设置、有相册... 跟用网站一模一样！ 👩‍🏫 老师比喻： NAS = 幼儿园的 全自动智能储物柜 🗄️✨ 你不用去柜子跟前 在家用手机扫一下 → 柜子的 控制屏幕传到你手机上 你在手机上点点点 → 柜子乖乖开门取东西 这个"控制屏幕"，就是NAS的 网页界面 ！ 🍳 第四课：前端和后端 —— NAS版本！ 还记得上次说的"大厨房"吗？ 你（浏览器）点菜 → 厨房（服务器）做好送来 NAS也是一样的，分成 前端 和 后端 两个部分！ 🎨 前端 —— 你看见的那一面 ┌─────────────────────────────────┐ │ NAS 网页控制台 │ │ ┌─────┐ ┌─────┐ ┌─────┐ │ │ │📁文件│ │🖼️相册│ │🎬视频│ │ │ └─────┘ └─────┘ └─────┘ │ │ ┌─────────────────────────┐ │ │ │ 这里显示你的文件列表 │ │ │ └─────────────────────────┘ │ │ [上传] [下载] [删除] │ └─────────────────────────────────┘ 这些你能看见的 = 前端 🎨 👩‍🏫 老师比喻： 前端 = 储物柜 正面的触摸屏 📱 漂漂亮亮的按钮、图标、列表 你能看见、能点的，都是 前端 ⚙️ 后端 —— 藏在里面干活的 你点击"上传文件" 👆 ↓ 前端说："收到！我去通知后端！" ↓ 后端收到指令 ⚙️： 1. 检查你有没有权限 🔐 2. 找到硬盘上空的位置 💾 3. 把文件存进去 ✅ 4. 告诉前端："存好啦！" ↓ 前端显示："上传成功！✅" 👩‍🏫 老师比喻： 后端 = 储物柜 里面的机械手臂 🦾 你在触摸屏上点"放东西进去" 机械手臂默默把东西码放整齐 你看不见它，但它一直在努力工作！ 🔄 前端和后端怎么说话？ 前端（网页） ←→ 后端（NAS系统） ↑ ↑ 你能看见的界面 藏在机器里的程序 它们用"API"互相说话 API = 两个人之间的"对讲机" 📻 👩‍🏫 老师比喻： 角色 NAS里是谁 幼儿园比喻 前端 网页控制台界面 储物柜的触摸屏 📱 后端 NAS的操作系统程序 里面的机械手臂 🦾 API 前后端通信接口 触摸屏和手臂之间的对讲机 📻

Everything is expensive. Treat yourself to one of these WIRED-tested and -approved Prime Day picks under $30.

The Moccamaster Prime Day deal is the best price I've seen this year, on a coffee maker that will probably outlive you.

I found the best protein powders that won’t make your morning smoothie taste like drywall.

You can save hundreds of dollars on Breville appliances during Amazon Prime Day.

The Samsara Tracking Label hides a BLE system that can offer real-time location in a disposable package.

I love building Lego sets, so here are my picks of the best Prime Day Lego deals. There are some great toy discounts, too.

Everything is expensive. Treat yourself to one of these WIRED-tested and -approved Prime Day picks under $30.

Everything is expensive. Treat yourself to one of these WIRED-tested and -approved Prime Day picks under $30.

The Fellow Series 1 espresso machine has its first sale, and it's a whopper. Other Fellow devices are 20 percent off, too.

Explore how my day as a senior leader looks now that I use 40 automations to help, and learn more about some of my favorites. The post I automated my job (and it made me a better leader) appeared first on The GitHub Blog .

Everything is expensive. Treat yourself to one of these WIRED-tested and -approved Prime Day picks under $30.

I recently gave a version of this talk at AI Engineer Europe in London. What follows is the fuller story — what we found when we looked at thousands of skills, what goes wrong, and how to fix it. You know that scene in The Matrix? Neo gets a spike in the back of his head, they upload kung fu directly into his brain, and he just... knows it. That's what a skill is for an AI coding agent. You write a markdown file — a SKILL.md — and the agent loads it when the task matches. Suddenly it knows your team's deployment process, or how your API handles pagination, or that you never use semicolons. It's not code. It's context. Procedural knowledge, injected at the right moment. The thing is — Neo's upload worked perfectly. Ours? Not always. Skills are everywhere now We spent some time analysing essentially all of public GitHub. In November last year, 12 repos had SKILL.md files. By March — five thousand four hundred and sixty. That's 450x growth in fourteen weeks. Skills went from zero to 27% of all agent config activity in three months. Faster adoption than CLAUDE.md , AGENTS.md , or any of the dotfile formats before them. And 1 in 12 merged PRs on GitHub now touches an agent config file — 8.4%, up from basically zero eighteen months ago. This is not a niche thing anymore. This is how people are working. Watch on YouTube But are they electrifying? Ninety percent of agent config files are never updated after creation. Write once, forget forever. Your codebase evolves every day. Your dependencies change. Your API contracts shift. But the instructions you gave your agent? Frozen in time. For Gemini files it's even worse — 97% are write-once. And the purpose-built "skill-as-product" repos? Over half are under 50 kilobytes. Wrapper repos. Many are AI-generated. High churn, low staying power. We have this explosion of skills, and most of them are going stale the moment they're committed. What we did about it The DevRel team at Tessl spent a couple of months doing something pretty

I wanted a smaller way to move books from my Mac to my Kindle. There are already good tools for this. Amazon has Send to Kindle. Calibre has a powerful content server. USB transfer still works. But each option carries a little friction: Send to Kindle goes through the cloud. Calibre is excellent, but it is a full library manager. USB transfer needs a cable and a manual copy step. Kindle Share is built for the smaller workflow: Choose a folder on your Mac. Start sharing. Open one local address on Kindle. Download the book. No account. No cloud. No cable. How it works Kindle Share starts a tiny local web server on your Mac. It only serves files from the folder you choose. When your Kindle is on the same Wi-Fi network, you open the Kindle browser and visit the address shown in the app. From there, you get a simple list of supported files and can download them directly. Supported formats today: PDF EPUB MOBI AZW AZW3 Why local-first? I like tools that do not require an account for a local job. If the book is already on my Mac and the Kindle is already next to me, sending the file through a cloud service feels heavier than it needs to be. Kindle Share keeps that path local. That also makes the app easier to reason about. The folder you choose is the folder being shared. Stop sharing, and the local server stops. What it is not Kindle Share is not trying to replace Calibre. Calibre is a serious ebook library system with conversion, metadata, plugins, and deep device workflows. Kindle Share is intentionally narrower. It is a small macOS utility for quick local transfer. Try it Download the latest build from GitHub Releases: https://github.com/thanhphuchuynh/kindle-shared/releases Source code: https://github.com/thanhphuchuynh/kindle-shared

Google DeepMind and A24 are teaming up to build AI filmmaking tools.