标签：#programming

submitted by /u/alexeyr [link] [留言]

submitted by /u/Happycodeine [link] [留言]

I really think it be monumental to do submitted by /u/HumbleEbb9857 [link] [留言]

There is no reliable “magic button” that turns an arbitrary HTML website into a clean, responsive, fully editable Elementor project. At first glance, converting an HTML website to WordPress sounds like a file-format conversion. You already have the design, text, images, CSS, and JavaScript. Why not upload everything, click Import, and continue editing the page in Elementor? The problem is that HTML and Elementor do not describe a website in the same way. An HTML page is the final output: a tree of elements styled by CSS and controlled by JavaScript. Elementor stores an editable model made of containers, widgets, global styles, responsive settings, and WordPress-specific data. A browser can render both results so that they look similar, but their internal structures can be completely different. What automated converters can do Modern converters and AI tools can read HTML, identify visual sections, and generate a rough WordPress layout. They are useful for prototypes and simple landing pages. Some tools can also copy styles or place the original code inside an HTML widget. But visual similarity is not the same as a production-ready Elementor website. A converted page may look acceptable on one screen while still containing: deeply nested containers; duplicated CSS; fixed pixel dimensions; broken mobile layouts; inaccessible elements; content that a client cannot edit. Forms, menus, sliders, animations, dynamic content, and custom JavaScript usually require separate work. The real challenge is rebuilding meaning, not copying pixels A human developer does not only see a rectangle with text. They need to decide whether it should become a Heading widget, a reusable global component, a dynamic WordPress field, or part of a template. The same applies to the rest of the page: Navigation must work with WordPress menus. Forms need validation, delivery actions, and spam protection. Repeated content may need posts, custom fields, or WooCommerce products. Fonts, colors, spacing,

The Future of Rust: Why This Memory-Safe Language is Dominating Systems Programming in 2026 In its early years, Rust was often viewed as a "rising star"—a promising language with significant potential but a steep learning curve. As we navigate through 2026, that narrative has fundamentally shifted. Rust has transitioned from a niche interest to a cornerstone of modern, memory-safe infrastructure. The language is no longer just proving its worth; it is defining the gold standards for systems programming, cloud-native backend services, and kernel development. This deep dive explores the key pillars driving Rust's evolution, its massive ecosystem growth, and the roadmap for the years ahead. Seamless Ownership: The New Era of Rust Ergonomics One of the most significant shifts in recent Rust development has been the relentless focus on language ergonomics. Historically, developers occasionally felt that custom smart pointers were "second-class citizens" compared to built-in references. The "Beyond the & " initiative has successfully bridged this gap. Through advancements in Smart Pointer Parity , developers can now use custom pointers—such as Rc , Arc , or specialized interop pointers—with the same intuitive syntax and capabilities as standard references. Furthermore, the introduction of sophisticated field projection mechanisms and &own references allows for unprecedented precision in managing ownership and borrowing. This makes complex data structures much easier to implement and reason about, drastically reducing the friction typically associated with high-level abstractions. The Async Revolution: Making Asynchronous Code Natural Rust has achieved a milestone often referred to as "Async Parity," aiming to make asynchronous programming feel as natural and seamless as synchronous code. Several key developments have fueled this revolution: Async-in-Traits: The stabilization of async fn in traits has removed the need for external crates like async-trait , greatly simplify

submitted by /u/mort96 [link] [留言]

Over the last few months, I’ve been turning small workflow problems I encounter on my Mac into focused utilities. Rather than building one enormous productivity suite, I wanted each app to solve a specific frustration well. I also wanted to build software the way I prefer to use it: local-first, available through a one-time purchase, and usable without creating another account or paying for another subscription. Here are five of the apps I’ve built so far. ScreenShelf My desktop used to become a temporary storage zone for screenshots, folders, documents, links, and files I needed for active projects. Folders helped with long-term storage, but they were not always useful for things I wanted to keep visible and nearby. ScreenShelf creates a customizable visual shelf for: Files and folders Screenshots and images Links Text Applications Frequently used project materials You can organize items across separate pages, customize the appearance of each page, and keep different groups of materials available for different projects. It also includes a Recents area that surfaces recent screenshots, which is helpful when the small screenshot preview disappears before you can interact with it. ScreenShelf is essentially the space between a cluttered desktop and a deeply nested folder system. Learn more about ScreenShelf PopNote Some reminders are too small for a full task-management system. You might need to remember to send a file in twenty minutes, check something after lunch, or complete one small step before ending the day. PopNote is a lightweight menu bar app that creates timed pop-up reminders on your Mac. The reminders appear as small visual bubbles rather than traditional notification banners. You can choose a time, add an icon, and let the note reappear when you need it. It is designed for temporary reminders that should remain noticeable without becoming another project to organize. Learn more about PopNote File Fetch I frequently download, save, rename, copy, and move

submitted by /u/Nouman-Rahman [link] [留言]

submitted by /u/namanyayg [link] [留言]

I am a sole freelance web developer, work has been slow lately so i had some time on my hands. Okay so, I have been making this maybe SaaS app which basically monitors 5 basic stats about any site you give it. and when any stat falls below the threshold it sends a email alert. It also provides a custom branded customer facing status page to give to clients, do you guys think custom domains for this is a imp feature? I am making this with freelancers in mind so they can add clients' sites here and can just keep an eye on 1 dashboard to monitor all sites for free. so, should i keep building this or am i wasting my time and tokens? live at: https://sitewell.darkentech.com submitted by /u/dark_knocker [link] [留言]

A visual explainer on the programming model behind KVM. A virtual CPU is just a host thread calling KVM_RUN , guest RAM is mapped into the process, and execution returns to userspace whenever the guest triggers a VM exit. It also covers /dev/kvm , hardware virtualization, EPT/NPT, QEMU, virtio and vhost. submitted by /u/Ok_Marionberry8922 [link] [留言]

The Quest Begins (The “Why”) I still remember the night our API started to sputter under a sudden traffic spike. Users were seeing 502 errors, the monitoring dashboard looked like a neon rainstorm, and I felt like I was stuck in a lobby waiting for the elevator that never arrives. We had a simple round‑robin load balancer sitting in front of three identical services. It worked fine when traffic was smooth, but as soon as a burst hit, one node would get overloaded while the others twiddled their thumbs. Honestly, I thought we just needed more servers. Throwing hardware at the problem felt like using a sledgehammer to crack a nut—expensive and messy. After a few frantic Slack threads and a lot of coffee, I realized the real issue wasn’t capacity; it was how we distributed the work. The balancer was oblivious to the actual load on each backend, treating every request like it was the same weight. That moment became my quest: design a load balancer that reacts to real‑time load, stays simple enough to operate, and doesn’t cause a reshuffling nightmare when we scale the cluster. The Revelation (The Insight) The breakthrough came when I read about least‑connections load balancing combined with a slow‑start period for new hosts. The core insight is deceptively simple: Send each new request to the backend that currently has the fewest active connections. Why does that work? Immediate fairness – If one node is handling long‑running requests, it will naturally have a higher connection count and receive fewer new ones until it catches up. Burst absorption – During a traffic spike, requests spill over to the less‑busy nodes instead of piling onto a single overloaded instance. Predictable scaling – When we add a new server, it starts with zero connections, so it gets a fair share of traffic right away—but we temper that with a slow‑start window to avoid overwhelming a cold host. Compare that to round‑robin, which blindly cycles through the list regardless of each node’s state. In

Most job search advice assumes a single problem with a single fix. In practice, people stall at different points, and the remedy for each one is different. Two tools I return to: resume.zoevera.com for resume targeting prepare.zoevera.com for interview practice, are useful because each handles one stage instead of claiming to handle all of them. Name the stage before choosing a fix The first question is where the process breaks down. Someone sending applications and hearing nothing back usually has a resume problem. Someone reaching interviews but not receiving offers has an interview problem. Someone applying to hundreds of roles with no response may be aiming at the wrong jobs. The fixes do not transfer between these cases, which is why generic advice tends to miss. Resume targeting is where most time gets lost This is the largest gap. Many people send one resume to every posting, then read the silence as a lack of qualifications. Often the resume does not match the language and priorities of the specific job, and an automated screen filters it before a person reads it. ZoeVera’s guide on why a resume stops getting interviews and its overview of how applicant tracking systems read a resume explain that mechanism in plain terms. The practical step is checking a resume against one posting before sending it. The match score check compares a resume to a job description and reports which terms are missing, and the keyword scanner shows the same gap at the phrase level. From there, the optimization walkthrough and the tool for matching a resume to a single posting close it. If your work is role-specific, the ATS resume tips library breaks the vocabulary down by profession, down to pages like software engineer resumes and nurse resumes . Interviews without offers is a separate problem Reaching final rounds and not converting them is rarely a technical gap. It is usually communication, composure, and how follow-up questions get handled. Practice helps more than reading ab

Array An Array is a collection of multiple values stored in a single variable. let fruits = [ " Apple " , " Mango " , " Orange " ]; Here, fruits contains three values. Why Do We Need Arrays? Without an array, you would write: let fruit1 = " Apple " ; let fruit2 = " Mango " ; let fruit3 = " Orange " ; Using an array: let fruits = [ " Apple " , " Mango " , " Orange " ]; This makes the code shorter and easier to manage. Array Index Each value in an array has an index. The index always starts from 0. Index: 0 1 2 ------------------------- Array: Apple Mango Orange Accessing Array Elements Use the index number to access a value. let fruits = [ " Apple " , " Mango " , " Orange " ]; console . log ( fruits [ 0 ]); console . log ( fruits [ 1 ]); // Output: Apple Mango Changing an Array Element You can update any value using its index. let fruits = [ " Apple " , " Mango " , " Orange " ]; fruits [ 1 ] = " Banana " ; console . log ( fruits ); // Output: [ " Apple " , " Banana " , " Orange " ] Finding the Length of an Array Use the "length" property. let fruits = [ " Apple " , " Mango " , " Orange " ]; console . log ( fruits . length ); // Output: 3 Adding Elements push() – Add at the End let fruits = [ " Apple " , " Mango " ]; fruits . push ( " Orange " ); console . log ( fruits ); // Output: ["Apple", "Mango", "Orange"] unshift() – Add at the Beginning let fruits = [ " Mango " , " Orange " ]; fruits . unshift ( " Apple " ); console . log ( fruits ); // Output: ["Apple", "Mango", "Orange"] Removing Elements pop() – Remove from the End let fruits = [ " Apple " , " Mango " , " Orange " ]; fruits . pop (); console . log ( fruits ); // Output: ["Apple", "Mango"] shift() – Remove from the Beginning let fruits = [ " Apple " , " Mango " , " Orange " ]; fruits . shift (); console . log ( fruits ); // Output: ["Mango", "Orange"] Looping Through an Array Use a "for loop" to print all elements. let fruits = [ " Apple " , " Mango " , " Orange " ]; for ( let i = 0 ; i < fruits . length ; i

Most tool lists for job seekers are just fifteen free things nobody had to think hard about. My filter is stricter now: a tool earns money only if it saved me time I'd have spent, removed an annoyance I actually felt, or made me better at something I do a lot. Templates failed the test for me. The screening software reads your resume, it doesn't look at it. Monthly resume builder subscriptions failed too, you finish the document in week two and pay through month four. What passed: checking my resume against each specific posting before applying. I use the free scan at ZoeVera for that and paid for a day pass only the week I had five applications to tailor. submitted by /u/Enough_Charge2845 [link] [留言]

This is about treesitter and LSP, specifically on their utility and recent journey into the Emacs core. Includes interactive visualizations of concrete syntax trees. submitted by /u/misterchiply [link] [留言]

I learned a lot of things by building a project from scratch: a backend DevOps tool that facilitates managing the fast update cycle in git dependencies. Overall, the system works by running two concurrent tasks: one to check dependencies, and the other to trigger workflows. When a dependency is updated, a workflow of the dependent GitHub repository is triggered. Initially, to pass data between tasks, I used an MPSC channel, but subsequently I transitioned to a database-backed queue because the channel was not resilient enough to crashes and network errors. In the article you can find more examples of unexpected quirks that I needed to iron out after the first iteration. submitted by /u/nilirad [link] [留言]

submitted by /u/TheSwedeheart [link] [留言]

I am an amateur developer (TS/Vue on the front) and build apps that are expected to run on both a desktop and a mobile. I may use some features specific to mobiles (a vibration for instance). Are there still limitations in PWAs in 2026 that make it so that everything a native app can do is not portable there? Note: this is specifically a technical question about the capacities of one technology vs another one. Please do not dive into "[PWA|Native] is better", except if there are technical reasons such as the above Note: Apologies if this is too basic of a question for r/programming , I can move it elsewhere if needed! submitted by /u/sendcodenotnudes [link] [留言]

I stared at the GitHub page for what felt like forever. The repo had thousands of stars, hundreds of issues, and a long list of contributors who clearly knew what they were doing. Me? I had a few small personal projects, some half-finished tutorials, and a nagging feeling that I wasn’t “ready” to contribute to real open-source software. Especially not an AI project with fancy models, complex pipelines, and people publishing papers off the codebase. But I wanted in. I wanted to learn how real-world AI systems are built, to get feedback on my code, and to be part of something bigger than my local src/ folder. So I made a deal with myself: no more waiting until I feel “ready.” I’d go from zero to my first pull request (PR) in one focused push. Here’s exactly how I did it, what I learned, and what I’d tell anyone hesitant about contributing to an open-source AI or machine learning project for the first time. Step 1: Pick the Right Project (Not the Biggest One) The biggest mistake I almost made was aiming for the most famous AI repo I could find. Big projects are great, but they can be intimidating and slow for a first-timer. Instead, I looked for: Active maintenance : recent commits, issues being closed, maintainers responding. Clear contribution guidelines: a CONTRIBUTING.md or at least a solid README. Beginner-friendly issues: labels like good first issue, beginner, or help wanted. Scope I could understand: I didn’t need to grasp the entire codebase, just enough to fix one small thing. I ended up choosing a mid-sized open-source AI library : not unknown, not legendary. Perfect. If you’re searching now, try queries like: “awesome open source llm” “open source machine learning projects good first issue” “open source AI tools GitHub” Then scan their issues tab for beginner-friendly tasks. Step 2: Set Up the Project Locally (Without Panicking) Once I picked a project, the next hurdle was getting it to run on my machine. The repo had a typical structure: project/ README.md