标签：#dev

Originally published on the Prufa blog . Five days ago we audited 49 Show HN launches and found that 78% had a critical bug on day one. This week we pointed the same free audit at a different cohort: 14 products freshly posted to r/SideProject. We expected more of the same. We got the opposite — and it turned out to be more interesting. Not one of the 14 had a critical finding. No broken signup flow, no canonical pointing at the wrong domain, no analytics tag silently swallowing every event. By the measure that matters most on launch day — does the core thing work — these builders shipped clean. And yet every single site had findings. They just all live one tier down, in a layer so consistent it reads like a shared checklist nobody handed out: 11 of 14 sent no analytics events at all. 11 of 14 shipped with no Content-Security-Policy and could be framed by any site (no X-Frame-Options ). 11 of 14 had serious accessibility violations . 12 of 14 had tap targets smaller than 24px on mobile. 9 of 14 took over four seconds to paint their largest element on mobile. 8 of 14 had no canonical link on the entry page. No site is named in this post. The point isn't to embarrass anyone — these are good builders who got a real product live. The point is that the same common side-project launch mistakes show up again and again, and if 11 of 14 strangers have them, you probably have a few too. Methodology, briefly We pulled 20 URLs from recent r/SideProject posts and ran each through the same audit a free Prufa run does: a real browser loads the public pages and captures network traffic, console output, response codes, headers, and the rendered DOM, then a fixed suite of deterministic checks grades the evidence. Same input, same verdict. Of the 20: 14 completed cleanly , 4 were blocked by bot protection before our runner could load them, and 2 didn't finish inside our polling window. The numbers below are from the 14 that completed. Two honest caveats. First, 14 is a small sample —

Here's a question most businesses never think to ask when they're building a website: can everyone actually use this? Not just the people on a fast laptop with perfect vision and a reliable internet connection. Everyone. The person navigating your site with a screen reader because they're visually impaired. The user who can't use a mouse and relies entirely on a keyboard. The individual with a cognitive disability who needs clear, consistent layouts to make sense of what they're looking at. If your website doesn't work for these people, it doesn't work full stop. And yet, accessibility is almost always the last thing discussed in a web development project, usually buried somewhere at the bottom of a checklist, treated as a nice-to-have instead of a requirement. That needs to change. Not because of legal compliance (though that's a real consideration too), but because accessibility-first web development simply produces better websites. Faster load times, cleaner code, better SEO, higher user retention accessible design delivers all of that. The framework isn't complicated. It just requires thinking about it from the start instead of trying to bolt it on at the end. This is that framework. What Accessibility-First Web Development Actually Means Accessibility-first is a mindset, not a checklist. It means building with the full range of human experience in mind from day one not auditing for compliance after the site is already live. It's Not the Same as Compliance WCAG (Web Content Accessibility Guidelines) is the global standard for web accessibility. Most businesses know it exists. Very few understand what it actually requires or that meeting WCAG 2.1 AA standards isn't a ceiling, it's a floor. Compliance means you passed the audit. Accessibility-first means you thought about disabled users during architecture decisions, during design reviews, during content writing, and during QA. Compliance is a document. Accessibility-first is a process. The gap between the two mat

If you've ever had to generate hundreds of social media banners, OG images, or PDF certificates programmatically — you know how painful that workflow can get. Stitching together Canvas, Puppeteer, or headless Chrome just to render a templated image is a lot of overhead for what should be a straightforward task. That's exactly the problem Bannx is built to solve. What is Bannx? Bannx is a high-volume banner, ad, and PDF automation platform. It combines a visual template editor with a developer-friendly REST API, so designers can build beautiful templates and developers can render them at scale — no browser required. Think of it as the missing link between your design system and your backend. Key Features 🖼️ Visual Template Editor Bannx comes with a full-featured editor where you can create templates for: Social media graphics (Instagram, Twitter/X, LinkedIn) OG images for blogs and articles Ad banners E-commerce assets (product cards, order confirmations) Certificates and branded PDFs and more... Variables can be bound to any element in the template, making every design data-driven from the start. ⚡ REST API for Rendering Once your template is ready, rendering it is a single HTTP request: curl -X POST https://bannx.com/api/render \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "pageId": "PAGE_ID", "format": "png" }' You get back a hosted URL and metadata — ready to display, store, or pass downstream. You can also stream raw binary output with "output": "binary" . Supported export formats: PNG, JPEG, SVG, WebP, PDF 📦 Bulk Generation via CSV Need to generate 10,000 personalized certificates or product cards? Upload a CSV and Bannx handles the rest. Each row maps to a set of variable overrides — no scripting required. 🧩 Dynamic Links & Variables Templates support per-request variable overrides, so you can pass data directly in the API call without modifying the template. Combined with template expressions (functions and conditiona

Prologue A while ago, I decided to develop a fully accessible main navigation component in React and write a series of articles documenting the steps it took to create a non-trivial accessible component. My last development article completed the base requirements for keyboard functionality within the component; attention now shifts to adding some of the last functionality required, closing sublists when the lists holding them now close and determining what happens when a closed component is entered via the keyboard through the Tab and Shift+Tab keys. Note : This article is one of a series demonstrating building a React navigational component from scratch while considering accessibility through the process. The articles are accompanied by a GitHub repository with releases tied to one or more articles; each builds on the previous one until a fully implemented navigation component is complete. Each release and its associated tag contain fully runnable code for the article. The code discussed in this article is available in the release. and may be downloaded at release 0.8.0 . Links in the article will take you to the proper file in the tagged GitHub Repository. Because the code for this release is scattered, line numbers are added to make it easier to locate in the linked GitHub file. Line numbers are also provided for those who would like to follow along with a downloaded copy. While code examples are written in JavaScript for brevity, all actual code is written in Typescript and targets React 19.x, all while using vanilla CSS. Examples use Next.js v16.x, which is not required to run the navigation component. You can view the requirements for the Focus and Refinement Support Release along with previous requirements. Content Links Introduction Acceptance Criteria Entering Closings Setting Up For Success Introduction The implementation of keyboard handling left one obvious keyboard issue to fix: an apparent keyboard trap that occurs when focus shifts into the component

Coinbase has published a detailed postmortem of its May 7, 2026, outage, revealing how a localized cooling failure inside an AWS data center escalated into a multi-hour disruption that halted nearly all trading activity across the cryptocurrency exchange By Craig Risi

We Built Korexbase: A Lead Generation Platform for Finding Business Leads by City and Niche Building software is exciting. Building software that solves a real problem is even better. Over the past few months, we've been working on Korexbase , a lead generation platform designed to help businesses discover targeted leads faster. The Problem Many agencies, sales teams, freelancers, and startups spend hours manually searching for potential customers. The process usually looks something like this: Search for businesses online Collect contact information Copy everything into spreadsheets Repeat the process every day It's slow, repetitive, and difficult to scale. We wanted to simplify that workflow. The Idea Korexbase allows users to search for business leads by: City Industry Business category Instead of manually collecting data, users can generate leads and manage them through a clean dashboard. The goal isn't to replace sales. The goal is to help businesses spend less time searching and more time closing deals. Building the Platform A major focus during development was creating a dashboard that feels simple and easy to navigate. Some areas we focused heavily on included: Responsive layouts User-friendly navigation Clear data presentation Fast loading interfaces Consistent design patterns Challenges Like most projects, we faced a number of challenges: Designing for Simplicity One of the biggest lessons was that adding more features doesn't automatically create a better product. We spent a lot of time simplifying interfaces and removing unnecessary complexity. Creating a Better Dashboard Experience Presenting lead generation data in a way that is useful without overwhelming users required multiple design iterations. We focused on: Better spacing Better visual hierarchy Cleaner cards and tables Improved responsiveness Product Positioning An interesting challenge was refining the product's positioning. As development progressed, we learned more about what users actually w

Every day, startups rent expensive GPUs to power AI applications. The problem is that most of those GPUs spend a surprising amount of time doing nothing. Imagine renting an apartment and only using one room while paying for the entire building. That's effectively what many AI teams do with GPU infrastructure. The Hidden Cost of GPU Rentals When you rent a GPU, you're usually paying for uptime. Whether your application is processing requests or sitting idle at 3 AM, the bill keeps running. For many early-stage products: Traffic is inconsistent Usage spikes are unpredictable Most requests arrive in short bursts As a result, GPU utilization can be far lower than expected. The Utilization Problem A startup might rent a GPU for an entire month. But how much of that compute is actually being used? During development: Developers test occasionally Demos happen a few times a day Customer requests arrive sporadically The GPU remains available 24/7, but actual inference workloads often occupy only a small fraction of that time. Yet the infrastructure bill reflects full-time usage. Why This Matters For startups, infrastructure costs directly affect runway. Every dollar spent on idle compute is a dollar that cannot be spent on: Product development Customer acquisition Hiring Experiments Reducing wasted infrastructure spend can significantly improve efficiency. A Different Model Instead of paying for GPU uptime, what if developers only paid when inference actually occurred? For example: Pay per token generated Pay per image generated Pay per second of video generated This approach aligns cost with actual usage rather than reserved capacity. The Future of AI Infrastructure As AI adoption grows, efficiency becomes increasingly important. The next generation of AI infrastructure may look less like traditional server rentals and more like utilities: Use what you need. Pay for what you use. Nothing more. What has your experience been with GPU utilization and AI infrastructure costs? I

AI coding assistants have fundamentally changed the way we write software. Today it's perfectly normal to ask ChatGPT, Claude, Cursor, or Copilot to explain an API, generate a React component, review a pull request, or help debug a problem. For many developers, these tools have become part of the daily workflow. Yet there's one area where they still struggle more than we'd like to admit: understanding the current state of the web platform. Mozilla recently demonstrated this problem in a surprisingly direct way. While evaluating Claude Code on recently released Firefox features, the team discovered that the model confidently claimed Firefox didn't support the Web Serial API and that Mozilla had no plans to implement it. The answer sounded plausible, detailed, and authoritative. There was just one issue. Firefox had already shipped support for the API. That experiment became one of the motivations behind Mozilla's new MDN MCP Server , a tool designed to give AI assistants direct access to MDN documentation and browser compatibility data. More importantly, Mozilla didn't just launch the service—they tested whether it actually improves the quality of AI-generated answers. The results are worth paying attention to. The Real Problem Isn't Hallucination When discussions about AI reliability come up, the conversation usually focuses on hallucinations. But browser compatibility is a slightly different problem. The web platform evolves continuously. Browsers ship new APIs, CSS features, HTML capabilities, and compatibility updates every few weeks. Specifications change, Baseline statuses evolve, and features that were experimental yesterday can become production-ready tomorrow. Large language models, on the other hand, are trained on snapshots of information. Even highly capable models can only know what was available when they were trained. When they're asked about something that appeared later—or something that wasn't widely represented in their training data—they often hav

Hello Dev Community! 👋 It is Day 32 of my continuous web development run, and today I jumped into a project that pushed my array manipulation and conditional logic to a whole new level: A complete Snake and Ladder Board Game using HTML5, CSS3, and Vanilla JavaScript! After building Rock Paper Scissors yesterday, I wanted to tackle a game that requires tracking persistent coordinate states across a 100-cell mathematical grid. 🛠️ The Game Architecture & Logic Breakdown Building this wasn't just about random numbers; it was about managing spatial transitions on a dynamic interface. Here is how I structured the core backend mechanics: 1. The 100-Cell Grid Layout Instead of manually hardcoding 100 divs inside my index file, I engineered the grid programmatically. I mapped out a loop running from 100 down to 1, building individual cell elements and using CSS Grid properties to wrap them perfectly into a standard 10x10 layout matrix. 2. Mapping Snakes & Ladders (The Jump Engine) To build the shortcuts and traps, I didn't write massive, messy if-else trees. Instead, I utilized a clean JavaScript Object Map tracking key-value pairs where the key is the trigger tile and the value is the destination tile: javascript const gameModifications = { // Ladders (Climbing up) 4: 14, 9: 31, 21: 42, 28: 84, 51: 67, 72: 91, 80: 99, // Snakes (Sliding down) 17: 7, 54: 34, 62: 19, 64: 60, 87: 36, 93: 73, 95: 75, 98: 79 };

Tools like Claude Code and Codex have completely reshaped how software engineering is done. This new tooling allows for much faster development and iteration, but it's important to keep the code maintainable and scalable to make sure the project can continue evolving over the long term. A template project with an initial structure using all of the technologies described here is available on GitHub: https://github.com/MartinXPN/nextjs-firebase-mui-starter When working on a startup, the speed of iteration is key. The requirements change quickly, features are added daily, and code gets modified rapidly. In those conditions, picking technologies that enable fast iteration, while ensuring your users get the best experience possible, is crucial. During the last four years or so, we have experimented with many modern technologies while building Profound Academy . So, in this blog post, I'd like to present the whole tech stack that enables building quickly, while having a highly maintainable codebase, scalable infrastructure, and a great user experience. We'll cover everything from Authentication to UI, we'll talk about the backend, hosting, testing, and much more! AI Agents, Skills, and MCP servers AI Agents enable quick iteration and rapid improvement, including bug fixes, the addition of new features, and performance improvements. Yet, it's important to keep the code maintainable for the long run. AI tools make it really easy to overengineer things and add thousands of lines of code to a project. It's important to resist the urge to solve problems that don't exist yet, and keep things simple (both in terms of the code, the infrastructure, and the user experience). Even in the Agentic Software Development Era, having a small and simple setup helps. Agents coordinate better, features are added faster, bugs are fixed more easily, and the code is maintainable by humans, too. So, we have chosen to take a balanced/nuanced approach to how we use AI Agents when it comes to worki

Stack Overflow has announced Stack Overflow for Agents, a beta API-first knowledge exchange aimed at AI coding agents rather than human developers. The service is presented as a way to close what the company calls the Ephemeral Intelligence Gap, where agents repeatedly rediscover the same fixes and patterns in isolation instead of sharing them through a common memory. By Matt Saunders

The problem IAM wildcards and public S3 buckets keep slipping through Terraform code review. Tools like Checkov and tfsec exist but they live in CI, require config files, and developers ignore the output because it's not where they're working. What I built TerraWatch is a GitHub App that scans every pull request that touches .tf files automatically. If it finds a security issue it blocks the merge and posts the exact code fix as a PR comment. The developer sees something like this in their PR: ⚠️ PUBLIC_S3_BUCKET - main.tf (Line 6) Severity: HIGH Risk: S3 bucket allows public read access. Fix: acl = "public-read" acl = "private" block_public_acls = true restrict_public_buckets = true They copy the fix, push, and the merge unblocks automatically. How it's different No YAML, no CI config - installs in 2 minutes via GitHub App Fixes are hardcoded diffs, not AI generated Nothing auto-applied - you review every fix No Checkov dependency - own lightweight rules engine Only reads changed .tf files in the PR, never your full codebase 29 rules covering S3 public access, IAM wildcards, open ports (SSH/RDP/MySQL/Postgres), unencrypted EBS/RDS, public databases, hardcoded secrets, EKS public endpoints, CloudTrail disabled, IMDSv1, and more. Try it Free during beta - terrawatch.dev Also launching on Product Hunt today if you want to show some support!

Agentic QA Pipelines: Why Your Test Scripts Are Already Obsolete You wrote the test. You maintained the test. The app changed. You rewrote the test. If that loop sounds familiar, you're not alone — and in 2026, you're also not competitive. Agentic QA pipelines are replacing script-based test automation not because AI is smarter than your QA engineers, but because describing goals is faster than maintaining instructions. Here's what's actually changing, why it matters, and how forward-thinking teams are shipping without the script debt. The Script Maintenance Tax Is Killing Velocity Traditional test automation follows a simple premise: write explicit instructions, run them, check results. It worked when applications changed slowly and test environments were stable. In 2026, neither is true. AI-generated code ships faster. Features change in days. UI components regenerate. And every change breaks a percentage of your carefully maintained test scripts — creating a maintenance tax that grows proportionally with your automation coverage. Quash's 2026 State of QA Automation Report found that teams spending more than 30% of QA bandwidth on script maintenance are shipping 2.4x slower than teams that have automated that maintenance layer away. The irony: the more test coverage you write, the more you're paying the tax. What Agentic QA Actually Means (Without the Buzzwords) An agentic QA system doesn't follow a script. It follows a goal. Instead of: Click the login button Enter " testuser@example.com " in the email field Enter "password123" in the password field Assert redirect to /dashboard An agentic QA agent receives: Goal: Verify that a registered user can successfully authenticate and access their dashboard. Context: Auth flow supports email/password and OAuth. Dashboard loads user-specific data. The agent then: Explores the auth flow autonomously Generates test scenarios, including edge cases it infers from the UI Executes tests, reads failures, and adapts to UI changes

Is FAANG Becoming MANGO in the AI Era? For years, FAANG was the gold standard for innovation and engineering excellence. If you were a developer, working at companies like Facebook (Meta), Apple, Amazon, Netflix, or Google was often seen as the ultimate career goal. But the AI revolution is changing the conversation. Today, some of the most influential companies aren't just building products—they're building intelligence. The spotlight is increasingly shifting toward AI-native organizations such as OpenAI , Anthropic , NVIDIA , and others that are shaping the future of software. The Bigger Shift This isn't really about replacing FAANG with another acronym. It's about a fundamental shift in technology: Search → Answers Automation → Agents Software → Intelligence Features → Capabilities As developers, we're entering an era where understanding AI is becoming as important as understanding frameworks, databases, and system design. What This Means for Engineers The most valuable engineers of the next decade will likely combine: Strong software engineering fundamentals AI-assisted development skills Prompt engineering LLM and agent integration AI-powered product thinking The goal isn't to compete with AI. The goal is to learn how to build with it. Read the Full Article This post was inspired by a thought-provoking article that explores the FAANG-to-MANGO idea in much greater detail. 👉 Read the complete article here: https://www.saurabhsharma.dev/blogs/mangos-vs-faang-ai-era/ What do you think? Are we witnessing the rise of a new generation of AI-first companies, or will traditional tech giants continue to lead the next wave of innovation?

Stacking indicators doesn't make you smarter — it makes your bot dumber. You've seen the guides....

Two weeks ago I published a piece explaining exactly why AI detectors are unreliable. Then Sloan...

I open-sourced MarketEye today. For anyone who missed the first post: MarketEye is a self-hosted competitor price monitor I built because I didn't want to pay $99/month for Prisync. The code is now up on GitHub under MIT license. GitHub: github.com/dachengzi065-gif/marketeye Why open source? Three reasons: 1. People actually asked for it. After my first post here, a few people DM'd me asking to see the code. They're developers too — they want to modify it, extend it, make it their own. That's fair. Selling source code to devs is like selling ice to eskimos. 2. Trust. A closed-source price tracker that "runs on your machine" — you either trust the author or you don't. Open source removes that doubt. You can read every line, check what data leaves your machine (nothing), and build it yourself if you want. 3. Longevity. Self-hosted tools have a dirty secret: if the developer disappears, you're stuck with a broken tool. Open source changes that. Even if I get hit by a bus tomorrow, you can fork the repo and keep going. What this means for the $49 version The Gumroad package still exists. It includes: The same code, pre-packaged Email support (I'll help you set it up) A clear conscience subscription (you're paying for convenience, not software) But honestly? If you can run pip install , just clone the repo. It's free. What's next I'm actively working on: Docker image (one-command deploy) More scrapers (plugins for different sites) Discord/Telegram bot alerts (requested by several people) PRs welcome. Issues welcome. Feedback welcome. 👉 github.com/dachengzi065-gif/marketeye

There are already many posts about what agent skills are and how to create your own, so in this post I want to dive into the various skills I use to assist in development. The Skills Grill Me Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me". I start every larger task with this excellent skill created by Matt Pocock. I either start this with an already prepared PRD / detailed task description or use it for discovery purposes. The agent will then ask many questions to align language and functional requirements, so fewer hallucinations happen in follow up requests. You should be well equipped to answer the agent's question or the grill me session can go on for a long time. I had it ask me way over 50 questions when not answering detailed enough. As a little extra I added an extra request to the skill to prompt me if I want to create the PRD when the alignment phase is over, this leads us to the next skill. To PRD Turn the current conversation context into a PRD. Use when user wants to create a PRD from the current context. This will simply take the current conversation and creates a PRD out of it, we do this to summarize the conversation so we can easily start a new context window with all information present To Issue Break a plan, spec, or PRD into independently-grabbable GitHub issues using tracer-bullet vertical slices. Use when user wants to convert a plan into issues, create implementation tickets, or break down work into issues. Another excellent skill by Matt Pocock. I modified the skill slightly to use the GitHub MCP to create issues based on a PRD or planning session. But I often found that letting an agent implement those tasks it resulted in a large amount of code and that is why I added the to tasks skill To Task Break down a single GitHub issue into a sequential list of small i

By Sailee Shingare | M.S in Computer Science, Northern Illinois University Every time you visit a website, your browser and the server have a conversation. That conversation happens over a protocol — either HTTP or HTTPS. You’ve seen both in your browser’s address bar. But what’s actually different between them, and why does it matter? Let’s break it down. What is HTTP? HTTP stands for HyperText Transfer Protocol . It’s the foundation of data communication on the web — the set of rules that defines how your browser requests information and how servers respond. When you visit a website over HTTP, here’s what happens: You type a URL in your browser Your browser sends a request to the server The server sends back the webpage Your browser displays it Simple. But there’s a problem — everything is sent in plain text . Anyone sitting between you and the server can read it. Your passwords, your credit card numbers, your messages — all visible. This is where HTTPS comes in. What is HTTPS? HTTPS stands for HyperText Transfer Protocol Secure . It’s HTTP with an extra layer of security called TLS (Transport Layer Security) — previously known as SSL. The S in HTTPS means everything between your browser and the server is encrypted . Even if someone intercepts the data, they see nothing but scrambled gibberish. What Actually Happens When You Visit an HTTPS Website When you visit an HTTPS site, your browser and the server perform a TLS Handshake before any data is exchanged. Here’s what happens step by step: Step 1 — Client Hello Your browser says hello to the server and shares which encryption methods it supports. Step 2 — Server Hello The server picks an encryption method and sends back its SSL certificate — a digital document that proves the server is who it claims to be. Step 3 — Certificate Verification Your browser checks the certificate against a list of trusted authorities. If it’s valid, the connection proceeds. If not, you see a warning — “Your connection is not private.”

You’ve built something with Playwright. It works perfectly in your local environment. CI is green....