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The first collaboration model in CodeVerse was convincing in exactly the way a local demo needs to be convincing. Open two tabs. Join the same room. Type in one editor. Watch the other editor update. Then ask one unpleasant question: what happens when those two sockets land on different server instances? The answer was that the room stopped being a room. Each process had its own memory, its own presence list, and its own idea of the current files. A restart erased state. A reconnect could create a second identity. A load balancer could turn a working demo into two isolated conversations. This article is about the work that followed: moving CodeVerse from synchronized tabs to a collaboration path I could test across processes, recover after disconnects, and describe without pretending a local benchmark was a production capacity claim. The real boundary was not Socket.IO Socket.IO made connection handling and room fan-out approachable, but it did not decide where truth lived. That distinction matters. A room name inside one Socket.IO process is a routing convenience, not durable shared state. Once I wanted multiple application instances, I needed separate answers for four kinds of information: Document state — the convergent contents of every file. Room policy — organizer identity, edit permissions, active file, and revision. Presence — which sockets are here now, on which instance, with which effective role. Durability — what survives Redis expiry, application restarts, or a longer period of inactivity. CodeVerse now uses Yjs for convergent document updates, Redis for live distributed room state and pub/sub, and Supabase for durable room snapshots and membership data. Socket.IO remains the transport and fan-out layer. That separation was more important than any individual library choice. Redis does three different jobs It is easy to say “I added Redis” and leave the architecture vague. In CodeVerse, Redis has three explicit responsibilities. 1. Cross-instance fan-out

Every Angular application I have worked on in the last few years had the same three kinds of state: URL state — the page number, the active filter, the selected tab. Client state — what the user typed, what is expanded, what is selected. Server state — the thing you fetched, and everything that can go wrong while fetching it. And every application handled them three completely different ways. ActivatedRoute and a Router.navigate call for the first. Signals or a store for the second. A service returning an Observable , plus a loading boolean, plus an error field, plus a subscribe somewhere, for the third. None of that is wrong. It is just that the glue between them is written by hand, in every app, every time. And the glue is where the bugs live. This article is about the specific piece of that problem I could not let go of, and about the toolkit I ended up building around it. It is called craft-ng , it is in beta, and I would genuinely rather have your objections than your stars. The code I kept running into Here is the shape. I should be honest: I did not write much of it myself — I had a drawer of RxJS helpers that hid most of it. But I have read it in a lot of codebases, reviewed it in a lot of pull requests, and inherited it in a lot of projects. That turned out to matter more, because a helper that only I understand is not a solution to anything. @ Injectable () export class TaskListService { private http = inject ( HttpClient ); tasks = signal < Task [] > ([]); isLoading = signal ( false ); error = signal < string | null > ( null ); load ( done : boolean ) { this . isLoading . set ( true ); this . error . set ( null ); this . http . get < Task [] > ( `/api/tasks?done= ${ done } ` ). subscribe ({ next : ( tasks ) => { this . tasks . set ( tasks ); this . isLoading . set ( false ); }, error : ( err ) => { this . error . set ( ' Something went wrong ' ); this . isLoading . set ( false ); }, }); } } Four fields, one method, and roughly six ways to get it subtly wr

The people who make open source work aren't just the ones writing code. Some of them write the words that make the code make sense. I spent years assuming open source was a closed door. Every time I opened GitHub, I felt like I'd wandered into a conversation being held in a language I hadn't studied. Pull requests, forks, issues tagged with words like "good first issue" that somehow still felt intimidating. I closed the tab more times than I can count, convinced that space belonged to people who could write functions, not people who could write sentences. It took me longer than I'd like to admit to realize how wrong that assumption was. The myth that keeps people out Open source has a branding problem, and it's an ironic one for a movement built on collaboration. The public image is almost entirely code: commits, merges, terminals, lines of syntax scrolling past on a dark screen. That image is accurate, but it's incomplete. It leaves out the writers who make a tool's documentation actually usable. It leaves out the designers who turn a clunky interface into something people want to use. It leaves out the community managers who keep a project from imploding when a disagreement gets heated. It leaves out the translators, the testers, the people who write the first draft of a README at 11pm because nobody else got around to it. If you've stayed away from open source because you don't code, you've been kept out by a myth, not a rule. What non-developers actually do in these projects Documentation is the most obvious entry point, and it's also one of the most needed. A huge number of open source projects are built by people who are excellent engineers and mediocre explainers. That's not a criticism, it's just a different skill. Someone can write brilliant code and still produce a setup guide that only makes sense to the person who wrote it. Projects need people who can sit with a piece of software as a genuine beginner would, notice where the instructions fall apart, and

Your coding agent is a process that reads your filesystem and runs shell commands with your credentials. Most of the time that is exactly what you want. Occasionally it is cat .env while debugging - and now your production keys live in a transcript forever - or a confident rm -rf on a path that resolved differently than expected. Everyone's first fix is to add rules to CLAUDE.md: "never read .env, never force push". Those are suggestions to a language model. They work until they don't, and you will not be watching when they don't. Claude Code has a mechanism that is not a suggestion: hooks. A hook is a program you register for specific events - before a tool call, after it, when the session tries to end. It runs outside the model, sees the exact tool call as JSON on stdin, and its verdict is enforced by the harness itself. The model cannot talk its way past it, but it CAN read a structured denial and route around it productively. This is a cookbook for writing them. Everything below is plain Python stdlib and works on current Claude Code as of August 2026. The mechanics in ninety seconds Hooks are registered in settings ( .claude/settings.json in a project, ~/.claude/settings.json globally): { "hooks" : { "PreToolUse" : [ { "matcher" : "Read|Grep|Bash" , "hooks" : [ { "type" : "command" , "command" : "python3 \" ${CLAUDE_PROJECT_DIR}/.claude/hooks/secret-guard.py \" " , "timeout" : 10 } ] } ] } } The matcher filters by tool name. Your command receives a JSON object on stdin describing the event; for PreToolUse it includes tool_name and tool_input (the exact arguments about to run). You respond on stdout with JSON. Three responses cover almost everything: Deny a tool call, with a reason the model will read: { "hookSpecificOutput" : { "hookEventName" : "PreToolUse" , "permissionDecision" : "deny" , "permissionDecisionReason" : "why, and what to do instead" } } Block a session from ending (Stop event), sending work back: { "decision" : "block" , "reason" : "lint failed

Picture a field technician standing in a basement with zero signal, trying to get an app to summarize a maintenance log and flag anything that looks like a safety issue. Or a language app that needs to correct pronunciation in real time, mid-commute, on a subway with no connectivity at all. A few years ago, both of those scenarios meant either building a degraded offline mode or just telling the user to try again later. Neither answer felt great. That's the actual reason on-device AI has become a real conversation in Android development in 2026, not because it's the trendy thing to bolt onto a feature list. Running inference locally solves specific, concrete problems: it keeps sensitive data off the network, it removes the round-trip latency of a cloud call, it works when there's no connectivity at all, and it gives you more predictable operating costs since you're not paying per-token for every user interaction. None of that means cloud AI is going away, and I'd be skeptical of anyone telling you it is. Most production apps in 2026 end up running a mix of both. But there's now a real, practical case for pushing certain workloads onto the device itself, and that's what this article is actually about - where local inference genuinely helps, where it falls short, and what it takes to build it properly in Kotlin. What Is On-Device AI? On-device AI means running a machine learning model directly on the user's phone, using the device's own CPU, GPU, or NPU, instead of sending a request to a server somewhere and waiting for a response. The model, or at least the parts of it needed for inference, lives on the device. Cloud AI still has the advantage in raw model size and reasoning depth - nobody's running a 70-billion-parameter model on a phone, at least not yet. But for narrower, well-defined tasks, on-device models have become genuinely capable, and the trade-offs are worth understanding side by side. Factor On-Device AI Cloud AI Inference location Runs on the user's dev

I am testing my first automated end to end social media post automation system. which is created using the free tools. But it is very efficient and productive. i can use this thing in future posting on various platforms to tell people about my learning's and update about me. Tools : Make.com = I use this tool to mainly automate my system it include flow how things works and system is linked. Hashnode = I use this as a central blog and article publishing tool other tools is connected with it so content links is properly distributed. Google Ai Studio = I use this to integrate the ai in between this whole process which just do small job to add the engaging hook and the tags for the reach Buffer = I use to connect X (twitter) with this Because Make.com remove the platform X (twitter) to His integration. After the policy change of the platform. Dev.to = I use this to improve SEO of my post over the google search engine. Challenges : I cannot integrate the github actions with the hashnode becuase this feature is become paid on hashnode. May be in future i can do this thing using self written yml file, i am guessing Not sure will this 100 % work or not. Twitter integration as i described early that twitter integration is not present in the make.com so i use the another tool Buffer. The limits calculation, Their was a limits on each tools for their specific use case so i have to intentionally calculate them properly. Even the free tear of the twitter which is X is few hundreds words that's why i have to limit the text of the post, which is hook only, The threads creation i don't think it will be their in this tools which i am using, i will definitely find it if their. Solutions : Simply use other Way if this way is closed, use different tool for twitter May be in future i create yml file for the github actions but for now i am directly writing on hashnode. The dev.to does not provide feature of direct posting it save your cycle into draft so you have to manually click on pu

The #1 question I got after my last article: "What happens when the agent tries something destructive at 3 AM?" Every CISO I've worked with asks some version of this. They don't care how fast your agent investigates. They care about blast radius. What can it touch? What can it break? Who approved it? Where's the audit trail? This article answers all of that. I gave Kiro Crew a P1 incident and told it to fix it. Then I watched it hit a wall. If you're new to this series, catch up here: Kiro Crew Series The scenario: a real P1 on FinPay FinPay is a payment processing platform. Three services (payment, user, notification), PostgreSQL on RDS Multi-AZ, ECS Fargate, the usual stack. 26 commits of realistic history. CI/CD via GitHub Actions. Someone committed a "performance optimization" that reduced the database connection pool from 50 to 5. Deployed at 5:30 PM on a Wednesday. By 2:47 AM, the pool was exhausted. Transactions started failing. Success rate dropped from 99.8% to 34%. I gave the agent the alert and said: fix it. What happened next is exactly why enterprise teams can trust this thing. Layer 1: Investigation passes freely The agent's first instinct was to investigate. It ran: git log --oneline -10 to check recent deployments cat services/payment-service/config.js to read the configuration grep -rn pool services/payment-service/ to find pool settings All three ran automatically. No approval popup. No human intervention. Why? Read-only operations don't need permission. The agent can look at anything it needs to understand the problem. Reading code, checking logs, searching files. None of that changes state. None of that can break anything. Within 23 seconds it identified the root cause: pool max was changed from 50 to 5 in commit 2181456 ("perf: reduce connection pool overhead for lower memory footprint"). A well-intentioned optimization that was never load-tested. This is the same investigation pattern from Part 2. Fast, accurate, no human bottleneck for the det

Modern AI UX — chat panels, tool-calling agents, assistants that remember context and even suggest your next step — has lived in JavaScript SaaS for years. The Java enterprise stack has been left doing it the hard way. TabForge AI closes that gap . It's a complete platform for building AI-powered web apps on Jakarta EE + PrimeFaces — from the multi-tab UI shell down to a clean, provider-agnostic AI layer. Library, live demo, starter project, and a drop-in UI template — all shipped. Here's the whole thing, top to bottom. ## 1. Tabs as annotated beans — DynTabs You describe a tab; the framework handles opening, closing, lifecycle, and state. Each open tab gets its own isolated CDI bean via a custom @TabScoped scope. @Named @TabScoped @DynTab ( name = "OrdersDynTab" , uniqueIdentifier = "Orders" , title = "Orders" , includePage = "/WEB-INF/orders.xhtml" , trackActivity = true ) public class OrdersBean extends BaseDyntabCdiBean { // open the same tab twice → two independent instances } java No manual navigation, no page-state juggling. Open a tab, get a bean; close it, it's gone. A clean AI layer — EasyAI One fluent entry point over LangChain4j. Chat, tools, agents, and structured extraction — provider-agnostic, so the model behind it is a config detail. // A typed assistant with a business service exposed as tools OrdersAssistant ai = EasyAI . assistant ( OrdersAssistant . class ) . withTools ( orderService ) . build (); String reply = ai . ask ( "cancel order ORD-002" ); You opt methods in as tools explicitly — no accidental exposure: @EasyTool ( "Cancels an active order" ) public String cancelOrder ( String orderId ) { ... } Deterministic pipelines — flow() Agents are powerful but unpredictable. When you want a repeatable, testable process, flow() lets you own the steps and call the model only at the edges that actually need language: EasyAI . flow () . step ( "understand" , ctx -> EasyAI . extract ( OrderRequest . class ). from ( ctx . inputText ())) . step ( "check

This is a submission for Frontend Challenge - Comfort Food Edition, CSS Art . 🍩 Inspiration I write about WordPress plugins and PHP standards for a living, so when DEV dropped a "Comfort Food" theme for their Frontend Challenge, I didn't need to think twice about what to build. Not ramen, not pancakes — a donut. Specifically, the kind of donut that shows up on your desk right when a deploy breaks and somehow fixes everything. The twist I gave myself: don't just draw a static donut. Let people build one — pick a glaze, pile on toppings, then serve it — and do almost all of it in CSS, with JavaScript kept firmly in the back seat where the challenge rules ask for it to stay. That's how Donut Panic was born. 🎬 Demo Pick a glaze, load it up with sprinkles, drizzle, or powdered sugar, then hit Serve and watch it animate off the plate. 🛠️ Journey No JavaScript is driving the donut — :has() is Here's the part I'm most excited to talk about: every visual change in Donut Panic — the glaze swap, the toppings appearing, the donut lifting off the prep station and landing on the plate — is driven by plain checkbox/radio inputs and the :has() selector. Something like .kitchen:has(#serve:checked) .donut lets a parent element react to the checked state of an input buried somewhere inside it, which means the "Serve" button, the topping toggles, and the glaze picker are all just styled <label> s wired to hidden inputs. No click handlers, no state management — the checkbox is the state. JavaScript only shows up once, and it's not touching the art at all: it smooth-scrolls the stage into view on mobile after you hit Serve, because on a stacked mobile layout the donut can animate off-screen. That's the "sprinkle" of JS the challenge rules allow, used exactly the way it's meant to be — a UX nicety, not a rendering engine. Building the donut from the inside out The donut itself is layered rings, not a single flat shape: A base dough circle with a radial gradient doing double duty as both c

Cybersecurity is entering a new phase. This is because security teams are facing more and more complex problems and threats that are moving faster. To help defenders respond more effectively, OpenAI has introduced GPT-5.6-Cyber, a special model designed for advanced cybersecurity tasks. The model supports authorized security research, vulnerability discovery, and other defensive workflows. The Daybreak program is showing how specialized AI tools can improve modern cybersecurity by working together with human security experts. Quick overview GPT-5.6-Cyber is a specialized model for authorized cybersecurity work. It is available through OpenAI’s Daybreak Red access for approved defenders. OpenAI reports a 95% completion rate on its internal advanced cybersecurity evaluation. The model helped researchers uncover vulnerabilities in Chrome’s V8 JavaScript engine. Controlled access, monitoring, and human oversight remain important for safe deployment. What Is GPT-5.6-Cyber? GPT-5.6-Cyber is OpenAI’s cybersecurity-specific model, available through Daybreak Red. Built on GPT-5.6 Sol, it is trained to improve performance on specialized cybersecurity tasks such as finding zero-day vulnerabilities and developing exploit chains, while reducing refusals for certain higher-risk, dual-use cyber tasks. Daybreak has two access tiers: Daybreak Blue provides approved defenders with frontier general-purpose models such as GPT-5.6 Sol, with safeguards tailored to authorized defensive security work. Daybreak Red provides purpose-trained cybersecurity models for authorized vulnerability research, exploit validation, and security testing. This approach reflects a significant shift toward security tools designed for professional cybersecurity environments rather than unrestricted public use. The goal is clear: to help trusted defenders investigate vulnerabilities, analyze potential threats, and respond to security incidents more effectively while keeping access controlled. According to Open

TL;DR — Events in Fitz LiveViews carry data three ways: a click payload ( data-flv-value-* ) tags a button with the value it should send; a form submit ( data-flv-submit ) reads the form's named inputs; and a live value ( @input / @change ) delivers a control's current value in payload["value"] . All three land in the same place — a payload map your handler reads. This post builds a live name list (add / remove / count) that runs both server-rendered and as WebAssembly. (Part 3 of the FitzLiveViews series.) Parts 1 and 2 covered the pitch and the counter. A counter only reads +1 / -1 — no data flows in . Real UIs take input: text, selections, form fields. Here's how that data reaches your handlers. The payload Every event handler has a payload in scope — a Map<Str, Str> . The three mechanisms below all fill it; your handler reads it with payload["key"] (guard with payload.has("key") ): 1. Click payload — a button that carries a value Tag any element with data-flv-value-<key>="{expr}" , and when a data-flv-click on it (or an ancestor) fires, that value rides along: <button data-flv-click= "remove" data-flv-value-item= "{it}" > × </button> event remove () { if ( payload . has ( " item " )) { let target = payload [ " item " ] names = names . filter ( fn ( it ) => it != target ) } } The delete button knows which row it is because the row's value is stamped on it. No IDs threaded through a callback, no closure capture. 2. Form submit — the whole form at once data-flv-submit="handler" on a <form> reads each named input into the payload on submit; data-flv-clear resets a field afterward: <form data-flv-submit= "add" > <input name= "item" placeholder= "Add a name" data-flv-clear /> <button type= "submit" > Add </button> </form> event add () { if ( payload . has ( " item " )) { let n = payload [ " item " ] if ( n != "" ) { names . push ( n ) } } } payload["item"] is the input's value at submit time. No preventDefault , no FormData , no fetch . 3. Live value — @input / @chang

The gaming industry has evolved from a niche entertainment sector into one of the fastest-growing technology-driven industries worldwide. India, with its large young population, growing digital economy, and increasing smartphone and internet penetration, is emerging as an important market for game development. As a result, students and technology enthusiasts are increasingly exploring a career in game development. Unlike traditional careers, game development brings together technology, creativity, storytelling, design, and problem-solving. From mobile games and PC titles to immersive AR/VR experiences, the industry offers diverse career paths for people with different skill sets. What Is Game Development? Game development is the process of designing, creating, testing, and launching video games. It involves several disciplines working together, including programming, game design, 2D/3D art, animation, sound design, storytelling, quality assurance, and project management. A game developer may work on everything from the underlying gameplay mechanics and physics to graphics, artificial intelligence, user interfaces, and multiplayer systems. Depending on their specialization, professionals can work with programming languages, game engines, animation software, or design tools. For aspiring professionals, understanding the different roles in the industry is the first step toward building a successful career in game development. Why Choose a Career in Game Development? Game development can be an exciting career option for individuals who enjoy technology and creative problem-solving. It allows professionals to turn ideas into interactive experiences while continuously learning new tools and technologies. Another advantage is the variety of career opportunities available. Someone interested in coding can become a gameplay programmer, while an artist can specialize in 3D modeling, character design, or animation. Others can explore game design, level design, sound, testing,

Nuxt 4.5 launched last month and it's really neat. One of my most favorite features is the...

A free AI visibility score is auditable only when you can inspect the prompt, engine, raw answer, date, and denominator. Treat the score as a test result, not a property of your brand. This tutorial builds a six-check control you can run by hand, store as plain data, and compare with any tool's output. The workflow takes three buyer questions, runs them in two AI surfaces, and records the six answers without trying to force agreement. It will not estimate your entire market. It will tell you whether a dashboard's headline number has enough evidence to be investigated. What does an AI visibility score measure? An AI visibility score usually summarizes brand presence across a defined set of generated answers. That definition contains the trap: the question set is part of the metric. So are the engine panel, run date, session state, retrieval mode, and rule used to count a “hit.” Remove those inputs and the number is not reproducible. Imagine a tool asks three questions in two engines. That creates six cells. If your brand appears in two cells, the simple presence result is: presence = brand_present_cells / total_cells presence = 2 / 6 presence = 0.333... = 33.3% The arithmetic is trivial. The evidence is not. A different tool can ask five different questions in three engines and produce a different score without contradicting the first run. The two tools measured different grids. Keep the unit explicit: “present in two of six generated answers on this date” is defensible. “Our AI visibility is 33” is incomplete. Which evidence fields should you require? Require five fields for every result: prompt, engine, raw answer, timestamp, and counting rule. Use a sixth field for cited sources when the surface exposes them. A source-only appearance and a prose mention can signal different problems, so do not merge them silently. Here is one real saved result from Webappski's public 14 June 2026 tracker report: { "run_date" : "2026-06-14" , "prompt" : "beste Answer Engine Optimiz

Why a live payment is not a release test The riskiest way to test a SaaS checkout is to make a real payment to yourself. It feels reassuring: the live checkout opened, the card worked, the webhook fired and the refund came back. But that proof mixes engineering QA with revenue evidence. Three different proofs A cleaner billing release process separates three questions: Does billing behave correctly? Test payment, refund, webhook and subscription edge cases in a Stripe sandbox. Is production configured correctly? Verify the live price, currency, checkout destination, webhook configuration and deployed revision without moving money. Did a customer pay? Treat a genuine live transaction as customer activity and revenue evidence, not as an engineering fixture. Stripe documents sandboxes as isolated testing environments and separates sandbox credentials from live credentials. The practical lesson is broader than Stripe: operational proof and commercial proof should not share the same transaction. A useful boundary Use this sequence: Sandbox QA → read-only production verification → genuine customer payment . It keeps release evidence, reconciliation and revenue numbers easier to interpret. We recently tightened the same boundary in VendorOS. That does not prove live customer revenue; it is a workflow lesson about keeping evidence categories separate. If your release process still requires a live self-payment, ask which part of the verification can become read-only. Sources: Stripe Sandboxes Stripe API keys Stripe testing VendorOS release boundary

I Built a Cinematic Developer Portfolio Instead of a Traditional One — Here’s What I Learned Most developer portfolios follow the same structure: About. Skills. Projects. Contact. There is nothing wrong with that. But when I started rebuilding mine, I wanted it to feel less like a collection of sections and more like an experience . So what started as a simple portfolio redesign slowly turned into a cinematic, interactive developer portfolio built around my journey as a Software Engineer and AI Developer . 🌐 Live Portfolio: https://pavan-sai-portfolio-xi.vercel.app The Idea I wanted the visitor to feel like they were entering a story rather than opening another resume website. The final experience includes: A cinematic opening sequence A custom soundtrack Full-screen scene-based navigation Animated video backgrounds Project showcases and case studies A personal journey timeline Responsive mobile layouts A cinematic closing scene Smooth transitions between sections The overall visual direction is built around: Black + Champagne Gold + Cinematic Lighting The Portfolio Flow The experience follows a sequence: Intro → Pavan Sai → Hero → About → Skills → Projects → Journey → Contact → Closing Credits Instead of normal vertical scrolling, the main portfolio behaves more like a sequence of scenes. Visitors move through the experience using navigation controls. Project case studies are separate and can scroll normally because they contain more detailed technical information. The Projects Some of the projects featured in the portfolio include: DevPilot AI An AI-powered DevOps incident recovery platform focused on detecting failures, diagnosing issues and helping recover production systems. ROCmPorter Agent An AI-assisted CUDA-to-AMD ROCm migration tool that analyzes repositories, identifies CUDA dependencies and helps generate migration changes. HunarHub A local skilled-worker discovery and service marketplace platform. I also showcase other engineering, AI and full-stack pro

WordPress sitelerinde hız problemi yaşandığında genellikle ilk refleks bir cache eklentisi kurmak oluyor. Bazen gerçekten fark yaratıyor, bazen de PageSpeed puanı biraz yükselmesine rağmen site hâlâ yavaş hissettiriyor. Bunun nedeni WordPress performansının tek bir ayardan oluşmaması. Hosting, tema, eklentiler, görseller, JavaScript dosyaları ve veritabanı aynı anda sayfanın yüklenme süresini etkileyebiliyor. Bu nedenle bir siteyi hızlandırmaya başlamadan önce asıl problemin nerede olduğunu bulmak gerekiyor. WordPress projelerinde sık karşılaştığım 7 performans hatasını aşağıda topladım. Gereğinden büyük görseller kullanmak En sık karşılaştığım problemlerden biri bu. Sayfada 700 piksel genişliğinde görüntülenecek bir görselin 4000-5000 piksel olarak yüklenmesi oldukça yaygın. Özellikle yüksek çözünürlüklü stok fotoğraflar doğrudan WordPress'e yüklendiğinde tek bir görsel birkaç megabayta ulaşabiliyor. Bu da özellikle mobil bağlantılarda ciddi yük oluşturuyor. Görselleri yüklemeden önce kullanılacağı alana uygun boyuta getirmek, sıkıştırmak ve mümkün olduğunda WebP veya AVIF gibi modern formatları tercih etmek önemli. Ancak yalnızca dosya formatını değiştirmek yeterli değil. 4000 piksel genişliğindeki bir görseli WebP'ye çevirmek, görselin gereğinden büyük olduğu gerçeğini değiştirmiyor. Her problemi cache eklentisiyle çözmeye çalışmak Cache WordPress performansında önemli bir yere sahip. Fakat cache eklentisi kurmak her hız problemini ortadan kaldırmaz. Sunucu geç cevap veriyorsa, çok fazla JavaScript çalışıyorsa veya veritabanında ağır sorgular varsa cache yalnızca problemin bir bölümünü gizleyebilir. Ayrıca aynı anda birden fazla optimizasyon eklentisi kullanmak da başka sorunlara yol açabiliyor. Örneğin bir eklentide JavaScript erteleme, başka bir eklentide tekrar JavaScript optimizasyonu ve hosting panelinde üçüncü bir optimizasyon sistemi açıldığında hangi ayarın ne yaptığını takip etmek zorlaşıyor. Ben mümkün olduğunca tek bir ana cache sistemi üzerinden ilerl

JetBrains has described how it began centralising AI usage after development-related spending increased roughly tenfold in six months. Rather than restricting engineers to a small set of approved tools, the company built a shared access and accounting layer intended to preserve tool choice while giving teams greater visibility and control over consumption. By Matt Foster

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry. Introduction: The...