标签：#dev

How Normal Software Engineers Actually Use AI in Their Daily Work Let's cut through the hype. You're not building the next AGI. You're a working software engineer with deadlines, legacy code, and a backlog that never shrinks. So how do you actually use AI tools in your day-to-day work? After surveying hundreds of developers and reflecting on real-world usage patterns, here's what normal software engineers are doing with AI—no Silicon Valley theatrics required. The Mundane But Invaluable: Code Completion and Boilerplate The most common use case is the least sexy: letting AI handle repetitive code. GitHub Copilot, Cursor, and similar tools excel at generating boilerplate that you'd otherwise copy-paste from Stack Overflow or previous projects. Real example: Writing CRUD endpoints in Express/TypeScript: typescript // Type this comment and let AI complete: // Create a REST endpoint for user registration with email validation app.post('/api/users/register', async (req: Request, res: Response) => { try { const { email, password, name } = req.body; // Email validation const emailRegex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/; if (!emailRegex.test(email)) { return res.status(400).json({ error: 'Invalid email format' }); } // Check if user exists const existingUser = await User.findOne({ email }); if (existingUser) { return res.status(409).json({ error: 'User already exists' }); } // Hash password and create user const hashedPassword = await bcrypt.hash(password, 10); const user = await User.create({ email, password: hashedPassword, name }); res.status(201).json({ userId: user.id, email: user.email }); } catch (error) { res.status(500).json({ error: 'Internal server error' }); } }); Did AI write perfect code? No. But it gave you scaffolding to refine, saving 10-15 minutes of typing. That's the real win. The Game-Changer: Explaining Legacy Code and Obscure APIs Every developer inherits someone else's mess. AI tools shine when deciphering undocumented code or unfamiliar libraries. Pract

You followed an old breeding guide. You put Penking + Bushi into your breeding farm, dropped the cake, waited for the egg... and out hatched Sibelyx , not the Anubis the guide promised. You're not alone. Your guide isn't broken. The recipe changed. When Palworld hit 1.0, the breeding table got quietly rewritten. Almost every pairing that players had memorized from early-access now produces a different Pal. There's no in-game notice, no patch note that lists the hundreds of changed combos — just a lot of confused hatchings. So I dug into the data. Here's what I found, and the small tool I built to make sense of it. What actually changed in 1.0 I compared two snapshots of the game's breeding data — the pre-1.0 set and the current 1.0 release — both sourced from the open-source PalCalc project. The headline number: 97.7% of comparable pre-1.0 parent pairs now produce a different Pal. That's not a typo. If you take the breeding pairs that existed before 1.0 and run them against the current data, nearly all of them hatch something new. A few concrete examples players keep running into: Old recipe (pre-1.0) What it makes now (1.0) Penking + Bushi Anubis → Sibelyx ... (more pairs in the tool) The mismatch matters because most breeding guides and calculators on the internet still show the old results. Players follow them, breed, and get confused. The subtle trap: renumbering vs. real change There's a detail that trips up every breeding tool that tries to track this. When Palworld 1.0 launched, it also renumbered parts of the Paldeck (Pal #139 vs #116, etc.). A naive diff tool would see the number change and wrongly report "the breeding result changed!" — when really only the number changed, not the actual Pal. To avoid that false signal, I match Pals by their internal game name , not their Paldeck number. A renumbering is not mistaken for a changed breeding result. Only genuine recipe changes are counted. The tool: PalShift I wanted a dead-simple way to answer one question:

I'm the developer of DeviceShelf, a local-first network scanner for desktop, mobile and a headless server edition. Until this week the iOS app only existed on TestFlight. Apple has now approved version 1.3.0, so for the first time you can get it straight from the App Store: DeviceShelf on the App Store . What the app does on a phone The iOS app is not a companion viewer. It runs the same scanning engine as the desktop version: it scans the network you're on, identifies devices (vendor, type, OS fingerprint), shows open ports per device, builds a security report, and raises presence alerts when devices appear or drop off. You can export and share results from the phone. The multicast entitlement iOS restricts multicast traffic for ordinary apps, and SSDP/UPnP discovery depends on it. Apple grants the multicast entitlement on request, and DeviceShelf's App Store build has it. In practice, UPnP/SSDP devices show up in scans on the phone the same way they do on desktop. Licensing The download is free and comes with a trial. Full features unlock in one of two ways: activate a DeviceShelf license, which covers desktop, mobile and the server edition with a single purchase, or use the in-app purchase upgrade on iOS. Pricing is on the website if you want the details. Local-first, on mobile too Scans stay on the device. There is no cloud account, and the AI-assisted device identification is bring-your-own-key; no key is bundled or required. The app is still young, and a phone is an unforgiving place for a network scanner. If it mislabels a device on your network or misses one entirely, I'd genuinely like to hear about it. Website: deviceshelf.app

Rspack, developed by ByteDance, has released version 2.0, featuring enhanced performance, reduced dependencies, and a focus on modern ECMAScript modules. Key updates include a pure ESM core, improved static analysis, and support for RSC. Performance benchmarks show big improvements in build times. The project has experienced substantial growth, with npm downloads exceeding 5 million weekly. By Daniel Curtis

A practical roadmap for students who want to build real AWS skills, create an impressive portfolio, and prepare for cloud engineering careers. Introduction Every year, thousands of students begin learning AWS. They watch training videos, collect certificates, complete online courses, and share digital badges on social media. Yet when internship interviews or entry-level cloud engineering opportunities arrive, many struggle to answer a simple question: "What have you actually built on AWS?" The cloud industry rewards practical experience, not passive learning. AWS itself focuses beginner learning on hands-on experience with foundational services such as Amazon S3, Amazon EC2, Amazon VPC, Amazon RDS, and cloud security because these services power most real-world cloud environments. If you're a student aiming for a career in Cloud Engineering, DevOps, Site Reliability Engineering (SRE), Solutions Architecture, or Platform Engineering, this article provides a practical roadmap that can help you become job-ready. Why Students Should Learn AWS Cloud computing has become the backbone of modern technology. Companies of every size use cloud platforms to: Host applications Store data Deploy AI workloads Build scalable systems Reduce infrastructure costs Improve reliability As a result, companies continue to hire professionals with cloud skills across software engineering, cybersecurity, DevOps, networking, and data engineering domains. AWS offers dedicated learning paths, hands-on labs, certification tracks, and career-focused programs specifically designed to help learners develop these skills. The key question is not: "Which AWS service should I memorize?" The better question is: "Can I design, deploy, secure, and troubleshoot cloud solutions?" The 5 AWS Projects Every Student Should Build Instead of completing another course, build these five projects. Project 1: Host a Static Website Using Amazon S3 What You'll Learn Cloud Storage Static Website Hosting Bucket Policies O

AI coding tools have gotten very good at one thing: generating code fast. What they haven't gotten good at is discipline. They don't know your architecture. They don't remember that you rejected a pattern last sprint. They don't know which parts of your context window are signal and which are noise. And they have no concept of a development workflow — no phases, no review gates, no verification steps. You describe what you want, they generate, and you hope the output fits. For small tasks this works fine. For anything that touches your real codebase at scale — refactors, new features with cross-cutting concerns, compliance-sensitive changes — the lack of workflow structure creates subtle, expensive problems that compound over time. We've been building Ortho to address two of these problems: workflow discipline (through ASES, a 6-phase AI development methodology) and context discipline (through a 9-component token optimization pipeline). This post explains both. The workflow problem with AI coding tools When a junior engineer joins your team, you don't just hand them a task and say "generate." There's a process: understand the codebase, plan the change, get the architecture reviewed, build it, test it, verify it, get it reviewed. The process exists because individual steps catch different categories of mistakes. AI coding tools collapse all of that into one step. You prompt. You get code. Done. The result isn't always bad code per file. The problem is architectural: the AI has no model of your layer boundaries, so it imports from layers it shouldn't touch. It has no memory of past decisions, so it re-proposes patterns you've already rejected. It has no verification step, so it confidently generates code that looks right but has subtle issues a reviewer would have caught in 30 seconds. The missing piece isn't a smarter model. It's a workflow. ASES: A 6-phase workflow for AI-assisted development ASES (v1.2) is the methodology built into Ortho's orchestration layer. It

A request starts at 10:00:00.900 and finishes 200 ms later. Your latency log says -800 ms . That sounds impossible until the machine corrects its clock between the two reads. Then ordinary code turns an adjustable wall clock into a broken stopwatch. The subtraction that works—until it does not This is probably hiding in one of your metrics helpers: async function timed ( operation ) { const startedAt = Date . now (); const result = await operation (); return { result , durationMs : Date . now () - startedAt , }; } Most of the time, this reports a sensible number. That is what makes it dangerous. Date.now() answers a calendar question: how many milliseconds have passed since the Unix epoch according to this machine? The answer must remain comparable with logs, users, and other computers, so synchronization software is allowed to correct it. A correction can change its rate, jump it forward, or move it backward. If either correction lands between the two calls, the subtraction measures the clock adjustment as if it were work. A timestamp is a coordinate. A duration is a distance. They need different instruments. Your computer already has two kinds of clock Operating systems expose several clocks, but two mental buckets cover most application code: Question Clock JavaScript example “When did this happen?” Wall clock Date.now() , new Date() “How long did this take?” Monotonic clock performance.now() A wall clock follows civil time. It has an epoch and can be serialized as an ISO timestamp. A monotonic clock starts at an arbitrary origin and promises that later readings will not be lower than earlier readings. wall: 1000 ── 1001 ── 0998 ── 0999 clock correction monotonic: 40 ───── 41 ───── 42 ───── 43 On Linux, CLOCK_REALTIME is settable wall time . CLOCK_MONOTONIC cannot be set and does not make discontinuous jumps backward, although gradual frequency adjustment can affect its rate. Linux even has CLOCK_BOOTTIME for a monotonic counter that includes suspended time. Thre

Open the network tab on almost any web app, trigger a failed request, and you'll usually find one of two things staring back at the user: a raw stack trace, or a message so generic it might as well say "something happened." Neither one helps. Both exist for the same reason they were written by developers, for developers, and never translated for the person actually using the product. The Error Message Nobody Designed Most UI elements go through some level of design scrutiny. Buttons get spacing decisions. Forms get validation states. But error messages? They're usually whatever string got thrown at the moment something broke, copy-pasted straight from a try/catch block into a toast notification. "Error 500: Internal Server Error." "Failed to fetch." "Unexpected token in JSON at position 4." These are diagnostic breadcrumbs for engineers debugging a system. To a user trying to submit a form or complete a purchase, they're just noise confirmation that something went wrong, with zero indication of what to do next. This is where good web app design services earn their keep not in the buttons and layouts everyone notices, but in the failure states nobody plans for until users start complaining. Why This Keeps Happening It's not that teams don't care. It's that error handling sits at the intersection of two disciplines that rarely talk to each other at the moment. Backend logic throws whatever exception the code produces. The front end just needs something to display so the app doesn't silently freeze. Nobody's job, at that moment, is to ask: "what should the user actually understand right now?" The result is a UI layer that's polished everywhere except the one place users encounter when things go wrong which, ironically, is exactly when clear communication matters most. What a Good Error Message Actually Does A well-designed error message does three things a raw exception never does. It tells the user what happened, in plain language not "Error: NetworkException," but "W

You ask an LLM for JSON. You get this: json { "name": "test", "valid": True, "items": [1, 2, markdown Three problems in one response: markdown fences the LLM wasn't supposed to add, True instead of true (Python literal), and the response was cut off mid-array. JSON.parse() throws on all three. A linter tells you what's wrong. But you're left fixing it manually. I kept hitting this wall so often that I built a dedicated repair pipeline: AI JSONMedic . Why existing tools don't cut it JSONLint / JSONFormatter — great for valid-ish JSON with one missing comma. Not built for LLM failure modes: they flag errors but don't repair them. jsonrepair (npm) — solid library, handles many cases. AI JSONMedic actually uses it as a last-resort fallback. But it doesn't tell you what it changed, and doesn't handle all the LLM-specific cases we needed. The 14 failure modes we target Through building this, we catalogued how LLMs specifically break JSON: Markdown fences — ` json wrapping the output Trailing commas — [1, 2, 3,] (the model "runs out" of items but adds one more comma) Python literals — True , False , None instead of true , false , null Single quotes — {'key': 'value'} instead of double quotes Smart quotes — "key" (curly quotes from copy-paste) Unclosed brackets — truncated at max_tokens mid-array or mid-object Unclosed strings — "value without closing Concatenated objects — {"a":1}{"b":2} when streaming produces multiple chunks NDJSON — newline-delimited JSON that needs wrapping Python-style comments — # this is a comment inside JSON JavaScript-style comments — // inline or /* block */ Escaped backslashes — \\n instead of \n Duplicate keys — same key appearing twice (ambiguous — we warn, not silently pick) BOM / encoding issues — UTF-8 BOM at start of response What makes the repair pipeline different Each pass targets one failure mode. The order matters — strip fences first, then normalize quotes, then fix commas, then close truncated structures. Each change is tracked. The

GitLab has introduced a new approach to Green DevOps, demonstrating how software engineering teams can measure the carbon emissions generated by their CI/CD pipelines. By Craig Risi

After spending countless hours designing, coding, debugging, and improving every little detail, I'm excited to share my personal portfolio with the developer community! 🌐 Live Website : https://didulagamage.pages.dev/ I'd Love Your Feedback ❤️ If you have a few minutes, I'd really appreciate it if you could visit my portfolio and share your thoughts. Your feedback helps me become a better developer. Thanks for reading! 🚀

I spent a week using video editing Skills to build a video editing Agent. It feels amazing! It can automatically edit a 30-minute video in just 10 minutes. Video editing Agent demo: automatically editing a 30-minute video in 10 minutes. I often use CapCut to edit talking-head videos, but after using it for a long time, I found several problems. Problem 1: Smart talking-head editing does not understand meaning Because it cannot understand the meaning, it sometimes fails to identify repeated sections. If I speak continuously for 20 or 30 minutes, editing the video myself becomes exhausting. Problem 2: The subtitle quality is poor The automatically generated subtitles contain many incorrect words and typos. So I used the Skills feature in Claude Code to build a video editing Agent. The fundamental difference is simple: CapCut vs. Agent: a fixed tool vs. an adaptive assistant. The key difference is: CapCut = fixed tool + manual operation Agent = adaptive system + automatic learning I am not replacing CapCut with a better algorithm. I am replacing it with a system that can continuously improve itself. But that is not even the most impressive part. The most impressive part is this: the more I use it, the better it understands me, and the faster it becomes. Three Core Designs 1. Agent Logic It only takes four steps. Video editing Agent workflow: from the video file to the final video. 2. The Skills System At first, I put every function into one large Skill. I had to add instructions to distinguish between different tasks, which was very inconvenient. Now I have separated the five core video editing tasks into five independent Skills and placed them in the .claude/skills/ directory. This makes the structure clearer and the tasks easier to select. When I enter /v , Claude Code automatically lists the five available Skills. The list of five independent Skills. I select one, and the AI runs that Skill. Simple, right? A manual task that used to take 10 minutes now only requires

GitLab has released version 19.2 of its DevSecOps platform, adding agentic automation aimed at the security and review work that has piled up as AI coding tools generate more code than developers can check by hand. The release, announced on 16 July 2026, brings four features out of beta or into public beta: Dependency Scanning Auto-Remediation, Security Review Flow, GitLab Duo CLI and Custom Flows By Matt Saunders

If you are a fellow blogger or web developer looking to build a high-performance, developer-friendly,...

So I built a Chrome extension that does one very stupid thing: it waits a random amount of time, then screams at you like a goat if you don't dismiss the notification fast enough. Simple idea. Should've been a simple build. It was not, because Manifest V3 said no. The plan was easy, in my head Here's what I thought I needed: chrome.alarms to fire at a random interval chrome.notifications to show a "you good?" popup If the user ignores it for 60 seconds, play a goat sound Step 3 is where things fell apart. Because in Manifest V3, your background script isn't a persistent background page anymore — it's a service worker. And service workers have the audio capabilities of a rock. No Audio() object. No Web Audio API. Nothing. You can't just do new Audio('goat.mp3').play() and call it a day, because there's no DOM for it to live in. I found this out the way everyone finds out things in MV3: by writing the obvious code, watching it silently fail, and then spending 20 minutes wondering if I'd misspelled "goat." Enter: the offscreen document Chrome's answer to "service workers can't do X" for a bunch of X's (audio playback included) is the offscreen document API . It's exactly what it sounds like — a hidden HTML page that exists purely so your extension has something with a DOM, so it can do DOM things. In my case, playing an mp3 of a goat losing its mind. The flow ends up looking like this: service worker (background.js) → creates an offscreen document if one doesn't exist → sends it a message: "play the sound" offscreen.html/offscreen.js → has an <audio> tag → actually plays the sound → closes itself when done It's a little bit like hiring a stunt double because the main actor (your service worker) isn't allowed near water. The offscreen document does the wet work, then goes home. Rough version of what that looks like in code: // background.js async function playGoatSound () { if ( ! ( await chrome . offscreen . hasDocument ())) { await chrome . offscreen . createDocument

I’m 14 and I got tired of spending hours (sometimes days) setting up new machines. Different distros, different package managers, fragile shell scripts, no rollback. So I built Nexus — a cross-platform environment provisioning engine. One command turns a fresh OS into a fully productive development machine. The Problem Setting up a dev machine is painful: Hundreds of Linux distros with different package managers (apt, pacman, dnf, apk). Shell scripts break easily with no rollback or state management. Tools like Ansible are overkill for laptops. Docker doesn’t configure your host. Dotfile managers don’t install dependencies. Result: Developers lose dozens of hours per year on setup issues. What Nexus Does One static Go binary. Detects your OS, chooses the right package manager, applies declarative YAML profiles, and handles everything with security gates and rollback. No scripts. No manual steps. Works on Linux and Windows (with WSL2 support). Standout Features Cross-distro support — apt, pacman, dnf, apk behind one interface. 7-step orchestrator with rollback — PreFlight → Refresh → Execute → Verify → Audit. Failed foundation packages trigger full rollback. Security gate (SanitizeAndExecute) — Allowlist, metacharacter rejection, timeouts. No raw shell execution. 10 built-in profiles — Go dev, Rust dev, Frontend, Data Science, Ethical Hacking, etc. WSL2 setup in ~60 seconds on Windows. Dotfiles + age-encrypted vault + Distrobox containers . Community profile registry . Optional Tauri GUI dashboard. Architecture Nexus is organized in bounded contexts: BRAIN — Cobra CLI + core engine (Go) DNA — YAML profiles with JSON Schema + struct validation + SHA256 integrity BRIDGE — WSL2 handling (cross-compiled) CONTAINER — Distrobox management VAULT — age encryption REGISTRY — Community profiles Every command goes through a strict security gate. State is crash-safe with atomic writes and append-only logs. Quick Start # Install go install github.com/Sumama-Jameel/nexus-engine/cm

I’ve been building a small set of calculator pages for freelancers and small-business owners. The first version was pretty simple: put the formula on a page, add a few inputs, show the result. That works for a demo. It does not always work for a real user. The more I worked on it, the more I realized the hard part is not the math. The hard part is making the page feel trustworthy when someone is using it for a business decision. Here are the changes that mattered most. 1. Explain the assumption close to the input At first I wrote explanations below the calculator. Most people never read them. So now I try to put small notes near the input itself. For example, if a calculator asks for payment processing fees, the user should not have to scroll down to understand whether that means percentage fee, fixed fee, or both. This is boring UI work, but it reduces bad inputs. 2. Show intermediate numbers A single final result can feel like a black box. For business calculators, I’ve found it helps to show a few intermediate numbers: subtotal estimated fees estimated tax net amount break-even number Even if the final number is the same, users trust it more when they can see how the result was built. 3. Avoid pretending the answer is exact Small-business math has a lot of messy edges. Taxes vary. Fees vary. Local rules vary. Some people include owner salary in cost. Some do not. A calculator should not act like it knows every detail. I now prefer wording like: estimated monthly amount or rough break-even point That feels less flashy, but it is more honest. 4. Make the empty state useful A blank calculator page is not very helpful. I started adding realistic default numbers or examples where it makes sense. Not because the default is “correct”, but because it helps the user understand what kind of number belongs in each field. This is especially useful for people who are not spreadsheet-heavy. 5. Keep the result easy to copy A lot of users are not trying to stay on the page. They

Look closely at what AWS, Microsoft, and Google shipped for AI agents this year and you notice something odd. Different names, different branding, and underneath, the exact same product. That convergence is convenient right up until you try to leave. Here's what they're all building and why it should make you a little cautious. What all three are actually building Strip away the marketing and the three big clouds are assembling the same five-part stack for running enterprise AI agents: a managed runtime to execute the agent, memory so it remembers context, identity so it can authenticate and be governed, tools so it can call your systems, and observability so you can see what it did. The products are AWS Bedrock AgentCore, Microsoft's Azure AI Foundry with its Agent365 control plane, and Google's Vertex AI agent stack. They look different on the pricing page. Architecturally, they're the same idea built three times. This isn't a coincidence. When you're solving the same problem, running agents safely inside an enterprise, you land on the same pieces. The market basically agreed on the shape of an agent platform in early 2026, and each hyperscaler raced to own it on their own cloud. Why the convergence feels great at first If your data and identity already live in one cloud, that cloud's agent runtime is the fastest path to production. Your storage is there, your identity system is there, your logging is there. The platform snaps into all of it. You get governance, audit trails, and deployment almost for free. For a team that just wants agents running with proper controls, that's a real gift. I get the appeal. The catch nobody puts on the slide Here's the trap. When your agent's runtime, credentials, state, and telemetry all live inside one cloud, moving it somewhere else isn't a config change. It's a rebuild. Take AWS Bedrock AgentCore. An agent built on it is wired to AWS identity, networking, and storage. Picking it up and moving it to Azure or Google isn't a matt

Every time I needed to check whether my microphone actually worked before a call, or whether a key on a used keyboard was dead, I ended up on some sketchy "free online tester" that wanted an account, showed six ad banners, and — the worst part — happily streamed my webcam to a server I knew nothing about. So I built the thing I actually wanted: TestOnDevice — 118+ device tests that run 100% in the browser. No installs, no account, and nothing you record ever leaves your machine. Here's what I learned building it. The core constraint: the network is off-limits The single rule that shaped the whole project: no device data touches a server. No webcam frames, no audio buffers, no keystrokes, no sensor readings. That constraint turned out to be freeing rather than limiting, because modern browser APIs are genuinely powerful. Almost every "device test" is just a Web API plus a bit of rendering: What you're testing The API doing the work Microphone / speakers MediaDevices.getUserMedia , Web Audio ( AnalyserNode ) Webcam getUserMedia , <video> , MediaStreamTrack.getSettings() Keyboard keydown / keyup , KeyboardEvent.code Mouse / touch / stylus Pointer Events, PointerEvent.pressure Gamepads Gamepad API MIDI keyboards Web MIDI API Display ( dead pixels , refresh rate ) Fullscreen + requestAnimationFrame Motion / orientation DeviceOrientationEvent , DeviceMotionEvent A live mic meter, entirely local The microphone test is a good example of how little you need. Grab a stream, wire it into an AnalyserNode , and compute RMS on each frame for a level meter — no recording, no upload: const stream = await navigator . mediaDevices . getUserMedia ({ audio : true }); const ctx = new AudioContext (); const analyser = ctx . createAnalyser (); ctx . createMediaStreamSource ( stream ). connect ( analyser ); const data = new Uint8Array ( analyser . frequencyBinCount ); function tick () { analyser . getByteTimeDomainData ( data ); let sum = 0 ; for ( const v of data ) { const x = ( v - 128 )

The feature was supposed to be the easy one. NotebookLM generates flashcards. My extension reads them...