标签：#AR

Let’s face it: if your current frontend optimization strategy still involves manually auditing codebases for missing useMemo hooks, micro-managing dependency arrays, or aggressively fighting layout shifts with complex client-side state management, you are wasting your engineering leverage. As we cross the midpoint of 2026, web framework architecture has quietly undergone a massive shift. We have firmly moved out of the era of manual performance tweaking and entered the era of automated, compile-time optimization . The goal of modern development is no longer just shipping fewer kilobytes to human users—it's also about optimizing data chunk delivery for AI web crawlers that evaluate your site in real-time. Here is how the modern full-stack ecosystem redefined performance this year, and what you should focus on instead. 1. The Death of Manual Memoization (Thanks, React Compiler) For years, React developers bore the cognitive load of rendering performance. One misplaced reference and your entire component tree re-rendered down to the root. With the absolute maturity and default adoption of the React Compiler across production frameworks, that paradigm is officially legacy code. The compiler handles component memoization automatically at the build step by analyzing javascript structures directly. // ❌ THE OLD WAY (Pre-2026 Manual Overhead) const ExpensiveComponent = memo (({ data }) => { const processedData = useMemo (() => computeHeavyMetrics ( data ), [ data ]); const handleAction = useCallback (() => { ... }, []); return < DataGrid items = " {processedData} " onAction = " {handleAction} " /> ; }); // THE MODERN WAY (Zero Performance Boilerplate) export function ModernComponent ({ data }) { const processedData = computeHeavyMetrics ( data ); const handleAction = () => { ... }; return < DataGrid items = " {processedData} " onAction = " {handleAction} " /> ; } Because the compiler injects optimization markers directly into the output code, human engineers can stop arguin

4-Phase Orchestration: How 5 Universal Agent Skills Achieve YAML-Driven Rules + Composable Components + Graceful Degradation When you're hard-coding your 3rd scoring if-else, maybe it's time to ask: can I move the rules into YAML and let the business change config instead of code? The Problem: Why Do Agent Skills Keep Reinventing the Wheel? Every Agent developer faces the same dilemma — every business scenario rewrites a similar pipeline : Scoring: Extract features → Match rules → Calculate score → Generate report Complaints: Extract ticket → Cross-validate → Pinpoint root cause → Archive Querying: Understand intent → Build SQL → Execute query → Render chart The skeleton is identical. What changes is only the "content" at each step. Yet every team builds pipelines from scratch. teleagent-skills offers an answer: freeze the skeleton into 5 universal Skills with 4-Phase orchestration, and let business changes live in YAML config only . Architecture Overview: 4-Phase Pipeline + 5 Universal Skills 2.1 4-Phase Orchestration Diagram ┌─────────────────────────────────────────────────────────────┐ │ Upper Business Skill │ │ (Scoring Engine / Evidence Chain / Data Aggregator / ...) │ └──────────┬──────────┬──────────┬──────────┬────────────────┘ │ │ │ │ ▼ ▼ ▼ ▼ ┌──────────┐┌──────────┐┌──────────┐┌──────────┐ │ Phase 1 ││ Phase 2 ││ Phase 3 ││ Phase 4 │ │ Extract ││ Analyze ││ Generate ││ Archive │ │ ││ ││ ││ │ │Info- ││Data- ││Report- ││Archive- │ │Extractor ││Analyst ││Generator ││Manager │ └────┬─────┘└────┬─────┘└────┬─────┘└────┬─────┘ │ │ │ │ ▼ ▼ ▼ ▼ ┌─────────────────────────────────────────────────┐ │ JSON Contract (Structured Data Contract) │ │ phase1_output.json → phase2_input.json → ... │ └─────────────────────────────────────────────────┘ Core idea: each Phase is an independent component, and Phases pass data only through JSON contracts . Any Phase can be replaced (want a more powerful Analyzer? Swap it out) Any Phase can be skipped (degradation mode) Any Phase c

Bringing the ideas I've been thinking about for months into life has never been easier, thanks to AI agents. The basic intuition is—give it a prompt, it builds the whole feature, the result looks good. Done. It takes only minutes to build the same thing that would've taken hours otherwise. Yes, I know, everyone's doing that. Right? The reason I'm opening like this is to point out what happened afterwards. I tried to use the search bar, and it fired a request on every keystroke. Wait, what? I didn't do that. Of course I'd add a debounce here. But the agent didn't. Why? I didn't ask it to. I said—build me a search bar, and it built me one that works; but I didn't say exactly what I wanted. Also, I noticed that the search button changes color on hover, but I'd already told it not to do that. The agent forgot, it hallucinated. What's missing then? What was missing was I did not provide the agent with the exact decisions to work with the feature; or did not provide a proper reference point to fallback to, to remediate the hallucination. In other words, I did not provide it with a proper spec. Hence, it took the hidden decisions itself; even though it pulled the feature off. This is the core problem that Spec-Driven Development (SDD) solves. The Hidden Product Decisions Your AI Agent Is Making For You Here's what happens when you describe something to an AI agent and it generates code: lots of decisions get made. Let's take the search bar implementation as an example. Does the filtering happen on the client or the server? Does the URL update so results are shareable? What does an empty query show? Everything, or nothing? I tend to miss nitty-gritty details while reviewing tons of AI generated code in a short amount of time. The code works, the UI looks right, I move on… Every one of those is a decision that belongs to my product. If I don't make the decisions consciously, the agent takes them based on whatever pattern shows up most often in its training data. Take that se

arXiv’s next chapter: Updates on our spin out from Cornell University: https://blog.arxiv.org/2026/06/30/arxivs-next-chapter/ submitted by /u/Nunki08 [link] [留言]

Smart speakers have spent the past few years searching for a compelling second act. Beyond music, timers, and controlling your lights, they've struggled to justify taking up space on the kitchen counter. AI promised to change that. Amazon debuted its new hardware powered by a revamped Alexa last fall, and now it's finally Google's turn. […]

With these high-tech automatic litter boxes, gone are the days of scooping and smells. Welcome to the future.

This hefty but nimble and highly customizable ebike makes the journey as important as the destination. Get where you want, and have fun along the way.

Welcome to the “Temu experience of telehealth,” where everyone from Grindr to MAGA influencers can open a virtual clinic selling weight loss drugs and more.

A researcher found that using Anthropic’s Claude Opus 4.7, he could break into the website of Front Gate—used by every festival from Lollapalooza to Bonnaroo—and freely issue any ticket he chose.

If you've submitted a BOM for quoting recently and gotten a lead time that made you do a double take, you're not imagining things. Passive component sourcing in 2026 is tighter than it's been in a few years — and MLCCs are the epicenter. I want to break down why this is happening, which component categories are actually at risk, and — more importantly — what you can do at the design stage to make your board less vulnerable to it. This isn't a "just wait it out" post; there are concrete layout and BOM decisions that meaningfully change your exposure. Why now? Three demand sources are converging on the same MLCC/inductor capacity that used to be dominated by consumer electronics: AI server infrastructure — GPU power delivery networks alone can chew through hundreds of decoupling capacitors per board, and hyperscaler order volumes dwarf typical consumer runs. EVs — automotive-grade passives (AEC-Q200, X8R/X7R) come from a narrower qualified supplier base, so even modest EV growth disproportionately tightens that segment. Renewables/grid infrastructure — pulling on high-voltage inductors and power resistors. On the supply side, new MLCC/ferrite production lines take 12–24 months to come online from the capital decision. Semiconductor fabs can reallocate capacity relatively fast; passive component fabs can't. That structural lag is the real reason lead times stretch out faster than they recover. Which parts are actually at risk Not everything is equally exposed: Category Normal LT 2026 Tight-Market LT Exposure Commercial MLCC (X7R, 0402/0603) 4–8 wks 8–16 wks Moderate–High High-density MLCC (0201, high µF) 6–10 wks 16–26 wks High Automotive MLCC (AEC-Q200, X8R) 10–14 wks 20–30+ wks Very High C0G/NP0 (precision/timing) 4–8 wks 6–12 wks Low–Moderate Power inductors (shielded, low DCR) 6–10 wks 12–20 wks Moderate–High Chip resistors 2–6 wks 4–8 wks Low Chip resistors are the least affected — manufacturing capacity is less concentrated and swapping vendors doesn't trigger a

I'm curious... What's the one feature that instantly makes you stop using a resume builder? For me, it was simple: You spend time creating your resume, everything looks great, and then the site asks you to pay just to download it. That experience inspired me to build Resumship, a resume builder where downloading your resume is completely free. Now I'm thinking about the next features to add, and I'd love to hear from the community. If you were building the ideal resume builder, what features would you include? AI-powered resume suggestions? Better ATS optimization? More templates? Portfolio integration? Cover letter generation? Something completely different? If you have a minute, I'd also love for you to try Resumship and share your honest feedback. 🌐 https://resumship.com Your feedback will directly influence what gets built next. Every suggestion, bug report, or feature request helps make the platform better for everyone. Looking forward to hearing your ideas! 🚀

Meta's Conversation Focus feature for smart glasses is now only free for three hours a month.

Adjust your options for things like account recovery, ad personalization, and which parts of your Google profile are shared publicly.

New satellite imagery reveals how much terrain has shifted in the wake of the twin quakes.

We all use AIs today - From a 5th grader to a retired pensioner, from a small-time business owner to a multimillionaire businessman, from a software engineer to a medical expert. AI exists everywhere! And to be honest its making our lives very simple. Yes, it does!. Response in no time, flexibility, reliability - yes, AI gives all and even more And as Software Engineers, we are getting more inclined towards AI. Back in the days, we used to rely on Stackoverflow to get our queries resolved. Sometimes it did, sometimes it didn't. But, AI changed that landscape completely - asking a query, retrieving data, asking follow-ups and so and on so forth. But, honestly, how many of us have thought - Wow this looks amazing! But how does it actually work! Let's say I type this in Chat GPT or Gemini or Claude etc: "Hi, how is the weather today?". The AI assistant takes the input and processes it and returns the response. But , there is a lot of processing and workflow happening under the hood. As a Software Architect, I struggled a lot to get these answers. Different sources, different suggestions. And the suggestions at some point seemed too overwhelming for me. So, I decided to break it down and start a series which will enable people to understand AI. I want to make people understand AI in the simplest way possible and make every developer leverage AI - not just to get their job done, but also to help in upskilling, so that they don't get lost in the overwhelming world of AI as I did initially! Follow me for more updates!

**Python Selenium Architecture** Introduction: Selenium automates web browsers. The Python Selenium architecture consists of four main components that work together to control a browser. 1.Selenium Client Library (Python Language Binding) Python developers write automation scripts using the standard Selenium API. This library converts your Python code into a standardized format. It sends these commands as programmatic requests to the browser driver. 2.W3C WebDriver Protocol/JSON Wire Protocol This is the communication channel between the code and the driver. Historically, Selenium used the JSON Wire Protocol over HTTP. Modern Selenium (Version 4+) uses the standardized W3CWebDriver Protocol. Commands and responses are transferred directly without any middle translation. 3.Browser Drivers Every web browser has its own specific executable driver. Examples include ChromeDriver for Chrome and GeckoDriver for Firefox. The driver acts as a secure HTTP server that receives commands. It passes these requests directly to the browser and returns results. 4.Web Browsers This is the final layer where execution happens physically. Supported browsers include Google Chrome, Mozilla Firefox, Microsoft Edge, and Safari. The browser receives commands through its native OS-level API. It executes actions like clicking, typing, or fetching text. Significance of Python Virtual Environments: A Python Virtual Environment is an isolated directory containing its own Python installation and independent packages. It prevents dependency conflicts across different software projects. Conclusion: Why It Matters? Avoids Dependency Hell: Different projects can use different versions of the same library. Protects System Python: Prevents breaking system-wide packages required by your operating system. Ensures Reproducibility: Allows developers to easily recreate the exact environment on other machines. No Admin Privileges Needed: Allows installing packages without sudo or administrator rights. Real-Wo

客戶開價太低嗎？Freelancer 接案前的 3 問決策樹 客戶說：「就改幾行代碼，收這麼多？」 你是不是也曾這樣懷疑過自己？ 每個 freelancer 都遇過這種時刻——客戶開了一個數字，你直覺「好像太低了」，但又說不出具體原因。以下是三個問題，幫你在 30 秒內判斷一個報價是否值得接。 3 問決策樹 Q1：這個價格是否覆蓋你的實際時間成本？ 別只算「改了幾行代碼」。真實成本包括： 讀懂陌生的 codebase（新手可能 3 小時起跳） 本地環境折騰（特別是別人維護的老項目） 測試和部署風險（部署壞了誰負責？） 客戶來回溝通的成本（「再大一點」「這個藍再淺一點」） 未知因素：如果代碼原作者已經不在，你是在維修「別人的技術債」 快速算法 ：把報價 ÷ 你估計的總小時數 = 每小時實際時薪。拿這個數字和你的底線比（建議：不是你「想要」的時薪，而是你「能接受吃飯」的時薪）。 如果低於底線 30%，進 Q2。 Q2：需求是否清楚到可以控制風險？ 報價低且需求模糊 = 高危信號。 以下任一癥狀存在，提高風險溢價或拒絕： 「就簡單改一下」——沒有定義邊界 沒有明確定義「完成」的標準——上線了算完成？客戶滿意了算完成？ 對方說「你先做再說」——這句話幾乎等於「我打算白嫖你」 沒有提供任何文件或代碼庫 access——等於讓你盲開 決策樹 ： 需求不清楚 + 報價低 → 報價必須上浮 50%，否則不接 需求不清楚 + 報價合理 → 可以談，先付定金再動工 需求清楚 + 報價低 → 進 Q3 Q3：這個案子是否帶來明確後續價值？ 有兩種情況可以在低報價下仍然接： 確定的後續項目 ：客戶明確說「這個做好了，下個月還有 X 個功能要做」 戰略性客戶 ：這個客戶有公開作品價值（大厂案例、知名公司、能寫進 portfolio 的上線項目） 如果兩者都沒有，低報價等於純粹的自我低估。 真實案例：隱藏成本解析 案例 1：$200 改 3 行 CSS 客戶說：「就改導航列的顏色，$200 應該夠了吧？」 表面看：3 行 × $66/行 = 天價。 現實： 理解整個樣式系統、找到正確的 CSS 檔案：2 小時 本地環境折騰（別人的專案，Node 版本衝突）：1 小時 反覆修改確認視覺效果：3 小時（客戶說「那個藍再淺一點、再加個 hover 效果」） 部署時發現壞了其他頁面：2 小時 客戶最後說「還是原來的好」：情緒成本 實際時薪 ：$200 ÷ 8 小時 = $25/小時，低於 freelancer 最低生存線。 案例 2：$2,000 報價改 2 天的「簡單項目」 客戶說：「做一個登入系統，就基本功能，2 個禮拜夠了吧？」 報價 $2,000，看起來還不錯。 現實： 需求訪談：4 小時（客戶一開始說「就登入」，後來才說「還要有第三方登入、密碼重置、邀請機制」） 設計資料庫結構：3 小時 實現 Registration + Login + OAuth：6 小時 測試覆蓋：4 小時 文件撰寫和交接：2 小時 實際 ：19 小時 × $105/小時 = $1,995 ——這個案子壓根不賺錢 常見陷阱：為什麼低報價 freelancer 總是吃虧 1. 「就幾行代碼」陷阱 代碼行數 ≠ 工作量。真正的成本在「理解上下文」——你得讀懂別人的代碼邏輯，這可能比你自己寫慢三倍。 2. 「簡單的 SQL」陷阱 每一條看似簡單的 UPDATE 語句，背後可能是： 凌晨 3 點資料庫突然鎖死 備份失敗、沒有測試環境 正式資料一個失误就沒了 3. 「長期合作」陷阱 客戶說「我們長期合作」通常是好事，但前提是—— 報價不能因為「長期」而打折 長期合作應該帶來穩定收入，不是穩定低價 你現在有一個具體報價嗎？ 如果客戶給了你一個數字，你不確定是否該接—— For $10, I'll review one client offer and tell you whether it looks underpriced, risky, or worth taking. 直接發報價截圖或文字到 paypal.me/cheapuno ，標註「報價審查」，24 小時內回覆具體分析。 快速決策檢查表（列印出來放桌邊） □ 報價 ÷ 預估時數 > 我的底線時薪？ □ 需求有明確定義邊界嗎？ □ 有隱藏的技術債或未知因素嗎？ □ 客戶有明確的後續項目或品牌價值？ □ 我有權利說「不」嗎？ 如果以上有任何一個「否」，這個報價需要重新談。 如果你想系統性学会如何報價、報價低了怎麼談、客戶不接受怎麼辦——歡迎從 Freelance Pricing Master Index 開始，這裡有 14 篇文章覆蓋 freelancer 定價的各種場景。

If you’ve spent any time poking around the AIE World’s Fair 2026 website, you may have come across...

The days of simply sitting in a plastic seat, eating a lukewarm hot dog, and watching a game with nothing but a physical scoreboard for context are officially over. Today, the sports world is undergoing a massive, tech-driven paradigm shift. Stadiums are no longer just concrete arenas; they are hyper-connected, edge-computing data centers . At the same time, live broadcasting is shifting from a passive, one-way viewing experience to an interactive, gamified reality. By combining next-generation stadium infrastructure with real-time, algorithmic micro-betting, the sports industry has figured out how to extract attention—and revenue—from fans every single second of a match. Here is a deep dive into the tech stack and engineering principles turning modern sports into a live-action video game. 1. The Smart Stadium Tech Stack: Infrastructure at Scale To engage tens of thousands of fans simultaneously in a single physical location, stadiums require enterprise-grade infrastructure capable of handling massive spikes in data throughput. When a touchdown is scored or a goal is disallowed, thousands of devices instantly pull video replays, refresh betting odds, and upload content. High-Density Wi-Fi 6E/7 and Private 5G Networks Traditional cellular networks quickly collapse under the density of 70,000+ fans. Modern venues like SoFi Stadium in Los Angeles or Allegiant Stadium in Las Vegas solve this using localized high-density networks: Wi-Fi 6E/7: Operating in the 6 GHz spectrum, these routers utilize wider channels (up to 320 MHz) and MU-MIMO (Multi-User, Multiple-Input, Multiple-Output) to beam dedicated streams to thousands of individual devices simultaneously without interference. CBRS (Citizens Broadband Radio Service) & Private 5G: Teams deploy private 5G networks using millimeter-wave (mmWave) technology. This provides ultra-low latency (< 10ms) and massive bandwidth, reserving dedicated lanes for stadium operations, point-of-sale systems, and premium fan applications.

I heard about DEV a while back from a former colleague who was posting regularly back then. She won a...