Everyone's debating whether coding is dead. I actually do this job.. with AI writing code beside me for most of my working hours. Here's what the headlines get wrong. Open your feed right now and you'll find the same headline in a dozen costumes: "Why AI will replace 80% of software engineers by 2026." "Is coding dead?" "Should you still learn to code?" It's the most-clicked anxiety in tech, and it's everywhere for a reason, it taps a real fear about real careers. But here's the thing about almost every one of those posts: they're written from the sidelines. Predictions about a job by people who don't do it. I'm writing this from the other side. I'm an engineer, and I drive AI coding agents every single day. They read code, write changes, run tests, and open reviews for most of my working hours. So when someone asks "should you still learn to code in 2026?" , I'm not guessing. Here's my honest answer: Yes. Absolutely. But the job you're learning for has quietly become a different job and almost nobody is telling you which one. The hype isn't entirely wrong Let me start by giving the doomers their due, because pretending the shift isn't real would make me exactly the kind of person I'm criticizing. The productivity jump is genuine, and it's not subtle. Industry surveys in 2026 put the share of new code that's AI-assisted somewhere north of 40%, and developers using these tools self-report double-digit speedups on routine work. That matches my experience. The agent now handles: Boilerplate and glue code —-> the stuff I used to type on autopilot, gone in seconds. First drafts —-> "scaffold something that does X" gets me 80% of the way instantly. Syntax recall —-> I stopped breaking focus to look up things I half-remember. Tedious refactors —-> rename-this-everywhere, migrate-this-pattern, done fast. and all the kludgy things that I dread to do. If your mental image of "coding" is typing syntax into an editor , then yes.. a big chunk of that is being automated. The vira

How I Fixed Bugs in 30+ Open Source Projects (And What I Learned) Over the past few months, I've been contributing to open source as an independent developer. No big company backing, no team — just me, a laptop, and a lot of caffeine. Along the way, I've submitted pull requests to 30+ repositories across the Python, JavaScript, TypeScript, and Rust ecosystems. Here's what I learned from the process — the good, the bad, and the "I wish someone told me this earlier." Why Contribute to Open Source? Let's get the obvious out of the way: it's not about the money (at least not directly). Most bounties pay $50-$500, and you'll spend 10-20 hours on a single PR if it involves deep codebase exploration. The real value is: Reputation — Each merged PR is a public signal that you can read, understand, and improve other people's code Learning — You'll see how major projects are structured, tested, and maintained Network — Maintainers remember helpful contributors. Jobs come from these relationships Scratching your own itch — Fix a bug that annoys you? Everyone benefits My Process: Finding Good Issues Step 1: Pick the Right Projects Not all projects are equally welcoming to new contributors. Here's my filter: Signal Good ✅ Bad ❌ Response time < 7 days > 30 days or never Issue labels good first issue , help wanted None CI/CD Green, fast builds Broken, 30min+ builds PR merge rate > 60% of open PRs merge < 20% merge Step 2: Find Issues You Can Actually Fix I look for: Bug reports with clear reproduction steps — Someone already did the hard work of identifying what's wrong Issues labeled easy-fix or similar — The maintainer thinks it's approachable Issues in domains I know — Don't pick a C++ compiler bug if you've never written C++ Step 3: Before Writing Code This is where most beginners fail. Don't start coding yet! Read the CONTRIBUTING.md — Every project has different style, commit message format, and PR requirements Look at recent merged PRs — What do good PRs in this project look

WordPress maintenance automation has a long-running market, especially outside Japan. ManageWP, MainWP, WP Umbrella, InfiniteWP — each has more than a decade of history behind it. While building our comparison pages, we surveyed all four side by side. An interesting pattern emerged: three things none of the four tools offer . Each is a gap the industry has long treated as "not feasible," and there are structural reasons why. Here's a look at those three unsolved areas — and why they remain unsolved. Gap 1 — Per-plugin updates with HTTP checks between each one In most maintenance tools, plugin updates run in bulk . After the batch, the tool takes a sitewide screenshot diff or HTTP status check, and if anything is broken, "Safe Updates" or "Atomic Updates" features roll everything back at once . Why isn't "one at a time with an HTTP check between each" the standard? The main reason is API design constraints . WordPress's built-in wp_ajax_update-plugin and Worker-plugin APIs (like ManageWP Worker) are designed around batch processing. Doing an HTTP probe from an external host after every single update would add significant per-update overhead. The industry has settled on "bulk update → bulk check" as the natural granularity. The side effect: identifying which plugin caused the breakage often falls to the operator's manual investigation. Gap 2 — Pinpoint rollback (only the one that broke) The industry-standard "Safe Updates" feature is fundamentally a "roll back everything" design. If 20 plugins are batched together and one breaks the site, all 20 updates revert. It's a safety-first choice — but operationally, it means the 19 that finished cleanly are also lost. Why isn't pinpoint rollback (revert only the one that broke) the standard? The root cause is state-management complexity . To pinpoint rollback, you need to keep the pre-update files of each plugin individually. Storage, transfer cost, and dependency consistency checks become impractical over a Worker-plugin HTT

If you're building autonomous trading agents on Solana, you've hit this problem: your agent needs to vet a token before it buys — but most agents trade blind. They have no idea whether a token is a rug pull, a honeypot, or has an active mint authority that can dilute holders to zero. I built RugCheck AI to fix this: an MCP server that screens any SPL / Token-2022 token for the common scam patterns, then executes the swap through an MEV-protected route — screening and execution in one place. What it checks Mint & freeze authority — is supply still mintable? Can your tokens be frozen in place? Honeypot detection — can the token actually be sold , or only bought? Liquidity & holders — concentration, LP locks, real depth. Token-2022 traps — permanent delegate, transfer hooks, default-frozen state. Tools verify_token_safety — full on-chain safety audit check_authorities — mint / freeze / Token-2022 trap detection simulate_sell — honeypot check execute_safe_swap — MEV-protected execution Connect RugCheck AI is listed on the official MCP Registry as io.github.MrWizardlyLoaf/rugcheck-ai . Remote endpoint, no install: https://web-production-58d585.up.railway.app/mcp Or self-host — it's open source (MIT) on GitHub: https://github.com/MrWizardlyLoaf/rugcheck-ai Why pre-trade screening matters A rug pull on Solana often looks fine at the moment of purchase — liquidity is there, the chart is green. The trap is in the authority : a live mint authority lets the deployer print unlimited supply after you buy; a freeze authority can lock your tokens so you can never sell. An autonomous agent that doesn't read these on-chain fields walks straight into it. RugCheck AI reads them directly and gives a verdict on a fresh launch instead of unknown . Built for Solana trading agents. Feedback welcome.

Google recently announced that WebMCP is entering origin trials in Chrome 149. The new WebMCP standard proposal lets sites expose tools (e.g., JavaScript functions and HTML forms) to in-browser AI agents, which can thus reliably simulate user actions instead of resorting to possibly expensive (e.g., on-screen reading) and often unreliable guesswork (e.g., DOM scraping). By Bruno Couriol

If your Magento store still depends on the old USPS Web Tools integration, you should assume your shipping rates are either already broken or one change away from breaking. That sounds dramatic, but it is the practical reality we have been seeing. USPS has moved away from the old Web Tools model and toward REST API v3 with OAuth 2.0 authentication. Magento's legacy USPS integration was built for a different era. For merchants, the symptom is simple: rates stop showing up, return inconsistently, or fail under conditions you did not use to worry about. For Magento developers, the reason is also simple: the built-in carrier module is not designed for the current USPS authentication and request model. This article explains what changed, why core Magento falls over here, how to migrate cleanly, and what to watch for whether you choose an extension or a custom build. What changed: USPS Web Tools is not the same platform anymore Historically, Magento's USPS integration talked to Web Tools-style USPS endpoints: structured shipping requests, legacy authentication, and XML responses. That is not the model USPS wants merchants using now. The modern USPS stack is based on: REST API v3 endpoints OAuth 2.0 for authentication Different request and response payloads Different onboarding and credential management patterns That shift matters because it is not just a URL update. It changes authentication, token handling, and request structure. In practical terms, a migration now means: Getting the right USPS developer credentials Exchanging those credentials for OAuth access tokens Updating the carrier request layer to use REST payloads Mapping the new response format back into Magento shipping methods If you skip any of that and try to "patch" the old module with endpoint changes, you are going to waste time. Why Magento 2's built-in USPS module no longer works Magento's built-in USPS module was not architected around OAuth-backed REST API calls. It expects a legacy carrier contract

When a Magento store feels slow, merchants usually notice it first on the homepage. When revenue actually slips, we usually find the damage deeper in the funnel. That was the case on a recent mid-market Magento 2 build we inherited. Product pages were acceptable. Search worked. But checkout analytics told a different story. Mobile users were stalling after address entry, re-clicking shipping methods, and abandoning before payment finished rendering. The merchant described it in business terms: "traffic is fine, but checkout feels fragile." They were right. The store was running a fairly typical Magento checkout stack: Luma fallback checkout, several shipping customizations, two payment methods, tax recalculation on step changes, and a handful of third-party scripts that had quietly accumulated over time. Together, they created a familiar Magento problem: too much JavaScript, too many render passes, and too much waiting on the highest-stakes route in the store. Over a 90-day measurement window after launch, checkout completion improved by 34%. Mobile completion improved by 39%. Lab metrics got much better immediately, and field metrics followed. This article covers why we chose React instead of Hyva Checkout, how we implemented the frontend, what moved the numbers, and what we would do differently next time. The problem with Magento's default checkout Magento's default Luma checkout is functional, but performance is rarely its strength. The architecture was designed around Knockout.js components, RequireJS modules, and a lot of UI behavior being layered in over time. Once a real merchant adds shipping estimation, fraud tooling, tax logic, payment widgets, analytics, and address validation, the route becomes busy in all the wrong ways. In this project, our baseline looked like this on a throttled mobile profile: Metric Before (Luma checkout) After (React checkout) Initial checkout route payload 1.8 MB transferred 486 KB transferred LCP 4.2s 1.1s INP 280ms 92ms CLS 0.1

A couple of years ago, I built a custom flashcard app. I had a huge list of words and sentences in Japanese that I collected in an Excel file. I wanted an app that could easily take them and display them on flashcards. The flashcard app was useful, but the main issue was that I could only use it on my laptop. This meant that when I wasn't home, I had no access to it. I made some updates so that I could deploy it to Azure and now I can use it on the train or at the park. I wanted to share the app and lessons learned during development. What It Does The app is a straightforward spaced repetition flashcard tool. You create collections, fill them with cards (front/back/optional notes), and review them. After each card you rate your recall: Button Meaning Easy Remembered without effort Good Remembered correctly Hard Remembered with difficulty Again Forgot (resets to day 1) Ratings feed the SM-2 algorithm, which is the same algorithm as other popular spaced repetition apps like Anki. Cards that are easy get pushed further and further into the future. Cards that are difficult will come back sooner. After a while, you're just reviewing what you actually need to review. There's also a 45-second timer per card. If it expires before you complete the card, it automatically counts as Again (Resets to day 1). Before the timer, I found it easy to lose focus or open another tab and forget about the current card. This has helped me stay focused for longer and stay on this task. The CSS is specifically designed to be mobile friendly. The Tech Stack Frontend: Blazor WebAssembly (.NET 10) Backend: ASP.NET Core minimal API (.NET 10) Database: Azure SQL (Basic DTU tier) Hosting: Azure Static Web Apps (frontend) + Azure App Service F1 free tier (backend) I mostly use C# at work, so Blazor WASM was a natural fit. The whole app shares models and flows together without jumping between languages. Importing Cards from Excel This Excel import function is one of the main reasons I made this app.

We've been a Magento agency in Chicago since 2008. When Hyvä Themes hit the ecosystem, we were skeptical—another theme promise. Then we measured Core Web Vitals on client stores and the case became obvious: Hyvä is the most practical path to a fast Magento storefront without a full replatform. This is the migration framework we use at Towering Media for US and Canadian merchants moving off Luma (or aged custom frontends) onto Hyvä. Why Hyvä now (not next year) Google's CWV thresholds affect ad quality and organic visibility. Luma checkout and catalog pages often ship 1.5–2+ MB of JavaScript before you add analytics, chat, and personalization. Hyvä replaces Knockout/RequireJS on the storefront with Alpine.js and Tailwind. Typical results on our projects: 50–70% less frontend JS on category and product pages LCP improvements of 1–3 seconds on mobile field data (highly variable by hosting and images) Lower maintenance — fewer JS conflicts between theme and extensions Delaying migration means paying for performance twice: once in emergency fixes, again in the eventual theme project. Phase 1: Discovery (1–2 weeks) Extension audit List every module that touches the frontend: bin/magento module:status | grep -v "Module is disabled" Flag anything with view/frontend , RequireJS , or Knockout in: Layered navigation and search Checkout and cart Page Builder widgets Blog and CMS enhancements Hyvä maintains a compatibility module ecosystem; unsupported extensions need replacements or custom Hyvä templates. Towering Media includes extension compatibility mapping in every Hyvä migration engagement. CWV baseline Capture before metrics from: Google PageSpeed Insights (origin-level) Chrome UX Report for key templates: home, category, product, cart Real-user monitoring if the client has it (GA4, SpeedCurve, etc.) Store screenshots. Stakeholders forget how slow the old site felt. Business constraints Document: Peak seasons (do not launch in November without war room

有時候我剛跑完一個指令，馬上發現其實只需要改其中一小部分。 例如我剛用 ffmpeg 轉了一個影片： ffmpeg -i calligraphy01.mp4 -c :v libx264 -c :a aac calligraphy_good_01.mp4 然後我想用同樣的指令處理下一個檔案： ffmpeg -i calligraphy02.mp4 -c :v libx264 -c :a aac calligraphy_good_02.mp4 如果用傳統方法，我可能會按上箭頭，然後手動把兩個地方的 01 改成 02 。 但在 zsh 裡，可以直接輸入： !! :gs/01/02/ 意思是： 拿上一個指令，把所有的 01 都換成 02 ，然後執行。 所以這個： !! :gs/01/02/ 會展開成： ffmpeg -i calligraphy02.mp4 -c :v libx264 -c :a aac calligraphy_good_02.mp4 語法是什麼意思？ !! 代表「上一個指令」。 :gs/01/02/ 代表「把所有 01 全部替換成 02 」。 所以完整的： !! :gs/01/02/ 意思就是： 使用上一個指令，把所有 01 改成 02 ，然後執行。 先預覽，不要馬上執行 有時候我不想讓它立刻執行，尤其是指令比較長、比較重要，或者執行成本比較高的時候。 這時可以加上 :p ： !! :gs/01/02/:p 它會只印出修改後的指令，不會執行： ffmpeg -i calligraphy02.mp4 -c :v libx264 -c :a aac calligraphy_good_02.mp4 如果看起來沒問題，就按一下 上箭頭 ，把剛剛印出的指令帶回命令列，再檢查一次，然後按 Enter 執行。 比較安全的流程就是： !! :gs/01/02/:p 確認輸出結果後： 上箭頭 → 再看一次 → Enter 這對比較長、比較容易打錯、或不想立刻執行的指令很有用。 只替換第一個地方 如果你只想替換第一個出現的地方，也可以用： ^01^02 不過像上面的 ffmpeg 例子，輸入檔名和輸出檔名裡都有 01 ，所以通常會比較適合用： !! :gs/01/02/ 這種小技巧每次可能只省幾秒鐘，但如果你常常在 command line 裡重複處理檔案、日期、編號、影片、圖片或 migration，累積起來真的會讓工作順很多。

Magento's default Luma checkout loads a heavy Knockout.js stack, dozens of RequireJS modules, and payment iframes that fight for the main thread. For merchants where checkout is the conversion bottleneck, shaving seconds off load and interaction time pays back faster than another homepage hero image. We rebuilt checkout in React— React Checkout Pro —for Magento 2 and Hyvä stores that needed Shopify-like speed without leaving Adobe Commerce. Here is what we measured, what surprised us, and what we would do differently. The problem: checkout is where Core Web Vitals go to die Homepage optimizations are table stakes. Checkout is different: More JavaScript. Payment methods, validators, shipping step observers, and third-party scripts stack on one route. More layout shift. Address suggestions, shipping method lists, and tax updates re-render large DOM regions. More input delay. Autocomplete plugins, reCAPTCHA, and BNPL widgets compete on keydown handlers. On a representative Luma checkout (mid-size US retailer, ~80 SKUs in catalog, 4 payment methods), lab tests before migration showed: Metric Luma checkout (before) React checkout (after) LCP (lab, 4G) 4.8s 2.1s INP (field interaction) 320ms 95ms CLS (full flow) 0.18 0.04 JS transferred (checkout route) ~1.9 MB ~420 KB Time to interactive (est.) 6.2s 2.8s Field data from CrUX lagged lab wins by 4–6 weeks but trended the same direction once cache and CDN rules settled. Your numbers will differ. The pattern we see repeatedly: the biggest win is shipping less JavaScript to checkout , not micro-optimizing the JavaScript you keep. Architecture: React island, Magento brain We did not headless the entire storefront. Magento still owns: Quote totals and tax calculation Shipping rate requests Payment tokenization and order placement APIs Customer session and cart persistence React owns the UI layer: step navigation, form state, validation UX, and optimistic updates while Magento APIs catch up. High-level flow: Browser → React Chec

Commerce dept. worries that a Fable 5 "jailbreak" could be a national security threat.

Anthropic isn't hiding its frustration. "We disagree that the finding of a narrow potential jailbreak should be cause for recalling a commercial model deployed to hundreds of millions of people," the company wrote in a blog post.

“The government believes it has become aware of a method of bypassing, or ‘jailbreaking’ Fable 5,” the company said in a blog post.

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