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TL;DR Built an embeddable support widget for a helpdesk product: no cookies — a short-lived bearer token in a header, hashed at rest. Entry is an HMAC-signed assertion from the host page. An adversarial review caught four real holes before launch. Outbound webhooks: sign the exact bytes, dedupe key for idempotency, SSRF guard on destination URLs. The requirement: end users file tickets from pages the product doesn't own. That means an embeddable widget — and embeddable means everything you know about sessions stops working. Why cookie-free The widget lives on customers' domains, so any cookie it sets is a third-party cookie — blocked or partitioned by modern browsers. Fighting that means flaky sessions, so: no cookies at all. The entry exchange mints a short-lived session token the widget sends in a header, and the server caches the session keyed by sha256(token) — a cache dump yields nothing replayable. Sessions last 60 minutes, and expiry shows a real recovery path in the UI instead of dying silently. customer backend widget (on customer page) helpdesk API | signs ref|email|name | | | into HMAC assertion ---> |-- redeem assertion (single use) ->| | |<-- session token (60-min TTL) ---| | |-- X-Widget-Token: ... ---------->| What the adversarial review caught Finding Fix Replay burn keyed by client-chosen nonce Burn by HMAC signature — a leaked assertion can't mint extra sessions `\ ` accepted inside signed fields Origin check failed open when Origin/Referer absent Fall back to the unspoofable Sec-Fetch-Dest header to enforce embedding Widget could request critical severity Clamp effective severity (including the channel default) to the widget's allowlist My favourite is the delimiter one. If you sign ref|email|name and accept | inside a field, two different identity tuples can share one valid signature. Canonicalization bugs, not crypto bugs. Webhooks out: sign the exact bytes Outbound webhooks get composed once at enqueue time and stored; the delivery job re-encod

In the first article of this series, we saw how a custom build-time compiler can transform a legacy Angular.js template into raw, optimized JavaScript. To recap, starting from this template: <!-- simple.html --> <p> Hello {{ name }}! </p> Our Go compiler generates the following JavaScript module: // simple.js export function template () { const p_0 = document . createElement ( " p " ); const text_1 = document . createTextNode ( "" ); p_0 . append ( text_1 ); return { mount ( container ) { container . append ( p_0 ); }, update ( change ) { if ( " name " in change ) { text_1 . data = " Hello " + change . name + " ! " ; } } } } This is incredibly clean. By running template() , we get an object with mount and update methods. Using mount is fully intuitive: we pass a reference to a DOM element, and it injects our empty paragraph ( p_0 ) into it: import { template } from ' ./simple.js ' ; const { mount , update } = template (); const container = document . getElementById ( ' view-container ' ); mount ( container ); // The DOM now contains: <p></p> (waiting for data) However, the paragraph remains empty until we call update with a change object like this: let changes = { name : " Mario " , }; update ( changes ); // The DOM surgically updates to: <p>Hello Mario!</p> But who is responsible for tracking changes in our application state, building this changes object, and calling update ? The answer lies in marrying the legacy Angular.js $scope with the modern JavaScript Proxy API . The Legacy State Pattern In a traditional Angular.js application, developers mutate the state directly inside a controller by assigning properties to the $scope object: // simple-controller.js export function SimpleController ( $scope ) { $scope . name = " Mario " ; } To bridge the gap between this legacy controller and our new build-time template, we need a way to automatically capture the assignment $scope.name = "Mario" and translate it into a structured update: let changes = { name : " Mario " }

Website performance is no longer just a nice-to-have feature—it's a critical factor for user experience, SEO, and business success. Even a one-second delay in page load time can reduce conversions and increase bounce rates. Whether you're building a portfolio, SaaS application, eCommerce platform, or business website, these optimization techniques can make a significant difference. Optimize Images Images are often the largest assets on a webpage. Use modern formats like AVIF or WebP, compress images, and serve responsive image sizes to reduce bandwidth usage. Self-Host Fonts Third-party font requests add latency. Self-hosting fonts, preloading critical font files, and serving only the required character subsets can dramatically improve loading performance. Remove Unused CSS & JavaScript Shipping unnecessary code increases download size and execution time. Tree shaking, code splitting, and removing unused styles help keep your bundle lean. Enable Caching Configure long-term browser caching for static assets and use hashed filenames for cache busting. This allows returning visitors to load your website much faster. Use Lazy Loading Images, videos, and iframes that aren't immediately visible should load only when needed. Native lazy loading is supported by modern browsers and is easy to implement. Optimize Core Web Vitals Google's Core Web Vitals measure how users experience your website. Focus on: Largest Contentful Paint (LCP) Interaction to Next Paint (INP) Cumulative Layout Shift (CLS) Improving these metrics benefits both SEO and user satisfaction. Minify Assets Minify HTML, CSS, and JavaScript files before deployment. Smaller files transfer faster and improve overall performance. Use a CDN Serving assets from edge locations around the world reduces latency and improves loading times for global visitors. Prioritize Accessibility Accessible websites provide a better experience for everyone and often align with SEO best practices. Use semantic HTML, descriptive labe

This is the story of a for-fun project, Luka Fit Index that started with me typing "ai for fun?...

Week 16 shipped the AI background remover: Rust-native, ort + ISNet + libvips, no Python. That was the feature. Week 17 was not about writing more of it. It was about the boring, high-leverage part that most side projects skip: turning one working feature into pages that can actually rank, and wiring those pages together so search engines can find them. No new engine code this week. Just leverage on what already existed. Here is what that actually looked like. The problem: a hub with nothing pointing at it The background remover lives at /remove-background . That is the hub. The plan was classic hub-and-spoke: one general tool page, then use-case spokes that each target a specific intent (removing a signature background, prepping an Amazon product photo, and so on). I built two spokes this week. But halfway through, I looked at how internal links actually worked on the site and found the real problem: nothing linked from the hub to the spokes. The spokes linked back to the hub in their body text, but the hub had no idea they existed. Neither did the ~180 converter pages. Tool links on the site were hardcoded in a frontend constant, roughly: export const IMAGE_TOOLS = [ { label : " Compress JPG " , href : " /compress/jpg " , tool : " compress " }, { label : " Resize Image " , href : " /resize-image " , tool : " resize " }, { label : " Crop Image " , href : " /crop-image " , tool : " crop " }, { label : " Images to PDF " , href : " /images-to-pdf " , tool : " convert " }, ] as const ; That list covered the converter tools. It did not include the background remover or its spokes at all. So the new pages were orphans: reachable only through the sitemap, with no internal links carrying any signal to them. For a domain that is still young and still earning Google's trust, orphan pages get discovered slowly and rank even slower. The fix: one constant as the source of truth Instead of hardcoding links in three different places, I made a single constant describe the whole cl

These may be the last days of Amazon’s Mechanical Turk.

A tour of the stack behind mattstratton.com and speaking.mattstratton.com: a monorepo holding two Astro sites and a dev.to sync tool, twenty years of blog posts, and the pipeline that crossposts posts like this one.

Episode 1/4 of the mini-series The week Supabase lied to me 4 times . The three following episodes cover a mutation silently swallowed by the SDK [CANONICAL URL EPISODE 2: to fill in after push], an RLS recursion resolved by a JWT hook [CANONICAL URL EPISODE 3: to fill in after push], and a query that stops at exactly 1000 rows without saying so [CANONICAL URL EPISODE 4: to fill in after push]. The Tuesday the security probe spoke It's 9:12am on a Tuesday in May. The daily drift probe has been running automatically for three weeks — an aclexplode query across all public objects, filtered on anon . I don't open it every morning. That morning, it's waiting for me with a row that has no business being there. Niran sets a coffee on the corner of my desk without a word. He reads the output over my shoulder. A PII backup table — personal data in plaintext, created two days earlier for a bulk reclassification — shows up in the list with SELECT , INSERT , UPDATE , DELETE granted to anon . Accessible to any unauthenticated curl request. He lets three seconds pass and says: "It's not RLS." Then he goes back to his hoodie. He's right. It's not an RLS bug. The table itself is open, at the GRANT layer, before RLS even applies. Three objects, three doors, one mechanism That week, I realize I'm not dealing with an isolated incident. Three distinct objects, in three different migrations, each open a door nobody thought they'd opened. The backup table first. Then a policy set TO public because the public landing page needs it, which lets a POST {} from anon through with an HTTP 400 NOT NULL response instead of 401 Unauthorized . And finally four SECURITY DEFINER functions written to execute transactional operations with their owner's privileges, all invocable by anon because EXECUTE defaults to TO PUBLIC at CREATE time. Three objects, three superficially distinct mechanisms, yet one shared root. At every CREATE , Postgres completes the migration with an implicit GRANT the author nev

The question you don't dare ask out loud It's 10:40 PM on a Tuesday, I just closed an ADR — the seventy-fourth in this setup, written conscientiously, dated, cross-referenced with its migration, its contract test, and the commit that triggered it. And the question rises, the way it always rises at that hour when you've been coding alone for ten hours: who did I just write this for . No tech lead to convince, no PR review that'll catch it, no hypothetical acquirer to reassure, no architecture committee to brief tomorrow. Just the file, just me, just the doubt. It's the question of a solo dev at 70 days of serious practice. It has an honest answer, and that answer is neither "it'll pay when you sell" nor "it'll pay when you hire". Those two ROIs belong to other trajectories. The ROI of the solo dev who documents is an ROI he buys himself — deferred, intangible at moments, but materially countable if you force yourself to measure it in the first person. Here's mine, over 74 ADRs and 18 doctrine rules accumulated in 70 days, with no external observer to validate the grid. The false economy of "I'll remember" First trap, the one that cost me three weeks before I learned the lesson. The solo dev believes he doesn't need to write down what he decided because he decided it himself — his memory is worth an ADR. False at 14 days, systematically false at six weeks. Not because general memory fails, but because technical memory has a deceptive shape: you remember perfectly that you decided , you no longer remember why you decided that way. Three weeks after the May 5 session where I wrote ADR-0051 (FK ON DELETE SET NULL + CHECK NOT NULL incompatible, DELETE failing silently), I reopen the migration to add a column. I reread the diff, I don't understand why a certain CHECK constraint is phrased like this — the alternative I mentally dismiss today seems simpler, and I'm two clicks from refactoring. I go check the ADR. The answer is there, dated, sourced, in three lines. The simpl

The evening Étienne asked to see the numbers Tuesday evening, end of the day, the open space had cleared except for Étienne. Étienne holds sixty percent of the house and spends his working week at a fund that acquires software publishers, and he looks at ERPs all year the way others read balance sheets. He sat on the edge of my desk, a metal water bottle in hand, and said what he always says when he senses someone is telling themselves a story. "What's that based on?" I was about to answer with a narrative. Sixty days of solo production on Rembrandt with Claude Code, learning the doctrine, the in-flight retractions, the incidents that hardened the rules. The declarative form was ready. But Étienne doesn't ask for a narrative, he asks for the material inventory. So I opened a terminal and let wc -l speak. This article is what I should have given him without waiting for him to ask — the dry, numbered balance, what worked, what didn't, what I would do differently. Not a success story, not a cautionary tale . Just the audit nobody runs on DEV.to because we're all too busy publishing the parts that shine. What's at stake behind Étienne's question is less the performance of a device than the possibility of measuring it honestly. Sixty days of practice with an AI assistant on a production project is a rare object at this stage. Most publications circulating on the subject are either brief demos from a hackathon or marketing announcements from vendors. The field return at sixty days, delivered with its numbers and retractions, barely exists. That's the gap I intend to close here, without more pedagogy than is strictly needed. The dry material inventory Sixty calendar days between the first session and today. Fifty-eight active days out of sixty , meaning two days without a commit and explaining why the rest of my life barely held. Over that window, the repo accumulated nine hundred and eighty-four commits bearing my name — an average of sixteen commits per working day, on d

"Not recommended for beginners." That's what the task sheet said about Level 3 of the Oasis Infobyte Web Development & Design internship. Naturally, that's the one I picked. The Task Level 3 has exactly one task — build a full-stack Pizza Delivery Application. Not a landing page, not a CRUD demo. A real platform: user authentication with email verification, a custom pizza builder, live payments, inventory management, an admin system, and real-time order tracking. The Stack React + Vite + Tailwind on the frontend, Node.js + Express on the backend, MongoDB Atlas for the database, Socket.IO for real-time updates, Razorpay for payments. Deployed across Vercel (frontend) and Railway (backend). What I Built The user journey: register → verify email (Nodemailer) → log in (JWT) → build a pizza in 4 steps (base, sauce, cheese, veggies) with dynamic pricing → pay through Razorpay's checkout → track the order live on a progress bar. The admin side: a separate authenticated dashboard managing a 20-item inventory with low-stock indicators and inline editing, plus order status management. When an admin updates an order's status, the customer's screen updates instantly — no refresh — via Socket.IO rooms per order. Behind the scenes: stock auto-decrements on every successful payment, a node-cron job emails hourly low-stock alerts, and Razorpay payments are verified server-side with HMAC-SHA256 signatures — never trusting the client. What Actually Taught Me Things The features were the syllabus. The debugging was the education. MongoDB Atlas DNS failures — my local machine couldn't resolve mongodb+srv:// connection strings because a VPN was interfering with DNS SRV lookups. Solution: the legacy non-SRV connection string format. Lesson: know what your connection string actually does. Railway's SMTP block — my deployed backend couldn't send verification emails because Railway's free tier blocks outbound SMTP ports entirely. No code fixes this — it's a platform-level restriction. I doc

A few years ago, writing code was the difficult part. Today, AI can generate an API, build a React component, explain Dynamic Programming, fix bugs, and even suggest architecture—all within seconds. So here's a better question. If AI can generate code, what exactly are companies hiring humans for? The answer isn't typing speed. It isn't memorizing syntax. And it certainly isn't copying solutions faster than someone else. The value of a developer is shifting. And learning platforms need to shift with it. The Developer Role Is Changing Modern software engineering is becoming less about writing every line manually and more about making good engineering decisions. Can you understand a problem before solving it? Can you identify why one solution is better than another? Can you improve AI-generated code instead of accepting it blindly? Can you build something that is maintainable, scalable, and useful? These questions matter more today than they did five years ago. AI Reduced the Cost of Writing Code One of AI's biggest achievements is reducing repetitive work. That's a good thing. Developers spend less time writing boilerplate and more time focusing on higher-level thinking. But this creates a new challenge. When everyone has access to the same AI tools, writing code becomes less of a differentiator. Thinking becomes the differentiator. Learning Needs to Evolve Too Many learning experiences still revolve around one objective: Solve another problem. Complete another lesson. Earn another badge. Those activities still matter. But in an AI-first world, they aren't enough on their own. Learners also need opportunities to connect concepts, apply knowledge, build projects, and understand why solutions work—not just that they work. Where PyNyx Takes a Different Direction PyNyx is being built around a broader learning journey rather than a collection of isolated activities. Instead of separating learning into unrelated pieces, the platform connects multiple stages of growth. Stru

Modern C# Features: A Deep Dive into Records, Pattern Matching, Async, and Performance A practical guide to the C# language features that have reshaped how we write .NET code — records, pattern matching, async/await improvements, nullable reference types, LINQ enhancements, Span<T> , and performance optimizations. Table of Contents Introduction Records Pattern Matching Async/Await Improvements Nullable Reference Types LINQ Enhancements Span<T> and Memory<T> Performance Optimizations Quick Reference Table Conclusion Introduction C# has evolved significantly since C# 8. Each release (9, 10, 11, 12, 13) has focused on three consistent themes: Conciseness — write less boilerplate to express the same intent. Safety — catch bugs at compile time instead of runtime (especially around null ). Performance — give developers low-level control without leaving the managed, safe world of .NET. This guide walks through the features that matter most in day-to-day development, with working code examples you can drop into a dotnet run project. 1. Records Introduced in C# 9 , record types give you immutable, value-based data models with almost no ceremony. Why records exist Before records, representing an immutable data object meant hand-writing a constructor, Equals , GetHashCode , ToString , and often a With -style copy method. Records generate all of this for you. // Before: a "plain" immutable class public class PersonClass { public string FirstName { get ; } public string LastName { get ; } public PersonClass ( string firstName , string lastName ) { FirstName = firstName ; LastName = lastName ; } public override bool Equals ( object ? obj ) => obj is PersonClass p && p . FirstName == FirstName && p . LastName == LastName ; public override int GetHashCode () => HashCode . Combine ( FirstName , LastName ); public override string ToString () => $"PersonClass {{ FirstName = { FirstName }, LastName = { LastName } }} " ; } // After: the same thing as a record public record Person ( stri

We have all been there. You walk into a room full of tech enthusiasts, the ambient noise is humming...

You did the hard part. You designed it, you built it, you shipped it. The product is good. And still, the users do not come. I have been in that exact spot more than once. You refresh the analytics, you tell yourself it is early, and quietly a worse question starts to form: what if people are not ignoring my app, what if they simply never see it? Here is the thing almost nobody tells builders in 2026. For a growing share of your future users, the front door to the internet is no longer a list of blue links. It is a sentence. Someone opens ChatGPT, Perplexity, or Google's AI Mode and types "what is the best tool for X." The model replies with a short list of names. If your product is not one of them, you do not exist in that moment. There is no page two to claw your way onto. There is one answer, and you are either in it or you are not. Three things are probably true about your app right now, and you cannot see any of them Your app might render blank to the machines that decide. If you built a single-page app (React, Vue, most modern stacks), the raw HTML a crawler receives can be an almost empty . Most AI crawlers do not run JavaScript. They read what your server sends and leave. To them, your beautiful app has no words, no product, no reason to be cited. You can rank number one on Google and still be missing from the answer. In one large 2025 study, roughly 68 percent of the pages cited in AI Overviews were not even in the top ten organic results. Ranking and being cited have quietly become two different games. Winning the old one no longer wins you the new one. A model may already be describing your product to strangers, and getting it wrong. A feature you do not have. A price that is out of date. A category that is not yours. You are being represented in rooms you will never enter, by a narrator you never hired, and the only way to fix the story is to give the machines a cleaner one to read. None of this shows up in your dashboard. That is what makes it dangerous

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

When you fire a fetch() and the component that triggered it unmounts, the request keeps going. The server still processes it. When the response arrives, it calls back into whatever JavaScript it finds — a stale closure, a dead state setter, a global store that has already moved on. React's "Can't perform a state update on an unmounted component" warning is the polite version of this. The silent version is worse: results from an old query overwriting the current UI. These aren't mysterious race conditions. They're the predictable result of starting async work and never telling it to stop. The race condition hiding in every search box The search input is the clearest example. The user types "reac", your debounce fires a request. Before it lands, they finish typing "react" and you fire another. Two requests, in flight at the same time, and no guarantee about which one finishes first. If the "reac" request happens to be slower — network jitter, a cache miss, a heavier result set — it will land after "react" and overwrite the correct results with the wrong ones. The bug reproduces maybe one time in twenty on a local dev server, and consistently in production on a slow connection. The fix isn't smarter debouncing. It's cancelling the previous request when a new one starts. AbortController in plain terms AbortController is a browser-native API for cancelling async work. You create a controller, pass its signal to fetch() , and call controller.abort() to cancel. If the response hasn't arrived yet, the fetch promise rejects with an AbortError . const controller = new AbortController (); fetch ( ' /api/search?q=react ' , { signal : controller . signal }) . then ( res => res . json ()) . then ( data => setResults ( data )) . catch ( err => { if ( err . name === ' AbortError ' ) return ; // expected — not a real error setError ( err ); }); // Somewhere else, when we no longer need this request: controller . abort (); Two things to internalize: signal is how the controller knows

Hello, DEV Community! 👋 Hi everyone! My name is Sulemana Abdallah , and I'm excited to be part of the DEV Community. I'm passionate about software development and enjoy building modern, responsive web applications using: HTML CSS JavaScript TypeScript React Python My goal is to become a skilled Full-Stack Developer and Software Engineer while continuously learning and building real-world projects. I joined DEV to: Learn from experienced developers. Share my projects and progress. Write about what I learn. Connect with developers from around the world. I'm looking forward to growing with this amazing community. Thanks for reading! 🚀

I am delighted to announce that a user reported dysfunction so that I could go down the rabbit hole...

I build one small browser tool a day and write down what I learned. Day 25 was a UUID generator. What started as "make some random IDs" turned into a proper look at how the bits are laid out, and why the newer v7 format is quietly the better default for a primary key. Live tool: https://dev48v.infy.uk/solve/day25-uuid.html A UUID is just 16 bytes with a few fixed bits A UUID is a 128-bit number, written as 32 hex digits grouped 8-4-4-4-12 . That is about 3.4x10^38 possible values, which is the whole point: any machine can pick one and trust it will not clash with any other UUID minted anywhere, ever. It carries no meaning — it is an identifier, not data. The reason UUIDs exist at all is coordination. The classic database ID is 1, 2, 3... from a central counter, and that works great until you have more than one writer. Two servers, an offline mobile app, or a sharded database cannot all ask one counter for the next number without a round-trip and a lock. UUIDs sidestep that entirely: each node generates its own IDs locally, with zero coordination, and they still do not collide. A client can even create the ID before the row ever reaches the server. Version 4: 122 random bits v4 is the one most people mean by "UUID". Fill all 16 bytes with cryptographic randomness, then overwrite two small fields so tools can recognise the format: const b = new Uint8Array ( 16 ); crypto . getRandomValues ( b ); // never Math.random() b [ 6 ] = ( b [ 6 ] & 0x0f ) | 0x40 ; // version 4 b [ 8 ] = ( b [ 8 ] & 0x3f ) | 0x80 ; // variant 10xx Two things get pinned. The high nibble of byte 6 becomes 4 — that is the digit right after the second hyphen, and it is how any parser knows the scheme. The top two bits of byte 8 become 10 , which is why the 17th hex digit of almost every UUID you see is 8 , 9 , a or b . Everything else stays random: 122 bits of it. Is "random and never collides" a contradiction? The birthday paradox says collisions become likely around the square root of the space, w