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WP Maintenance Manager ships from a single Python codebase to both Windows and macOS. "Python is cross-platform — write once, run anywhere," the saying goes. The reality is that the distribution pipeline is completely separate per OS , each with its own pitfalls. PyInstaller / Inno Setup / Apple Notarization / eSigner — the release cycle is a combination of OS-specific toolchains. Here's the full picture, plus what to watch out for at each step. (The choice of internal architecture, Flask + browser UI, is covered separately in why we built a desktop app on local Flask + browser UI ; this post is about distributing that architecture across two operating systems.) The per-OS pipeline at a glance Step Mac Windows Build PyInstaller ( --target-arch x86_64 ) PyInstaller Distribution format .app bundle → .dmg folder → .exe installer Installer creation hdiutil / create_dmg.sh Inno Setup ( .iss script) Code signing codesign + Developer ID certificate eSigner CSC (cloud signing) Pre-distribution validation Apple Notarization SmartScreen reputation buildup Final artifact WP_Maintenance_Pro_X.X.X.dmg WP_Maintenance_Pro_Setup_X.X.X.exe Both OSes share PyInstaller, but the path diverges from there. Mac sits inside Apple's review process; Windows runs through Microsoft's reputation system. They're fundamentally different ecosystems. Mac — PyInstaller → sign → Notarization → DMG The Intel / Apple Silicon trap The first trap in Mac PyInstaller builds is architecture . Running pip install + python build_app.py on an Apple Silicon Mac without thinking produces native binaries (like cffi ) for arm64 only — which then don't run on Intel Macs at all. The fix is to run the entire build through arch -x86_64 : arch -x86_64 pip3 install -r requirements.txt arch -x86_64 python3 build_app.py That produces an .app containing only x86_64 binaries, which runs natively on Intel Macs and through Rosetta 2 on Apple Silicon — a unified distribution. Sign inside-out The .app PyInstaller produces conta

We’ve all been there: it’s 2 AM, you’re deep in a "Refactoring Rabbit Hole," your coffee is cold, and your heart is racing. You feel productive, but is your body paying the price? As developers, we often ignore the physical signals of burnout until it's too late. In this tutorial, we are going to quantify the "Dev Grind." We'll build a Programmer Stress Warning Dashboard using the Oura Ring API to track Heart Rate Variability (HRV) and correlate it with your GitHub commit frequency . By the end of this guide, you'll have a real-time visualization of how that complex Kubernetes migration is actually affecting your nervous system. We will be utilizing HRV monitoring , biometric data visualization , and the Oura Ring API to create a predictive stress model for high-performance engineers. The Architecture 🏗️ The logic is simple: we fetch your physiological "readiness" and stress markers from Oura and overlay them with your activity from GitHub. If your commits are spiking while your HRV is tanking, it's time to step away from the keyboard. 🥑 graph TD A[Oura Cloud API] -->|HRV & Stress Levels| B(Next.js Backend) C[GitHub API] -->|Commit Frequency| B B -->|Data Aggregation| D{Correlation Engine} D -->|JSON Stream| E[D3.js Visualization] E -->|Alerts| F[Developer Dashboard] style F fill:#f96,stroke:#333,stroke-width:2px Prerequisites 🛠️ To follow this tutorial, you'll need: Oura Ring & a Personal Access Token (from the Oura Developer Portal ). Next.js (App Router) for our frontend and API routes. D3.js for crisp, reactive data visualizations. Vercel for instant deployment. Step 1: Fetching HRV Data from Oura API Heart Rate Variability (HRV) is the gold standard for measuring autonomic nervous system stress. A high HRV usually means you're recovered; a low HRV means you're under pressure. Here is a clean implementation of a Next.js API route to grab your daily stress metrics: // app/api/oura/route.ts import { NextResponse } from ' next/server ' ; export async function GET (

When I started coding, I made so many silly mistakes 😅 Today I’m sharing 3 small mistakes that every beginner developer makes: 1. Trying to write "Perfect Code" on Day 1 Bro, your code will be messy at the start. Just make it work first. Perfect comes later. 2. Watching tutorials but not coding yourself Watching videos is easy. But you only learn when you type the code on your own laptop. 3. Getting scared of errors Red error ≠ Failure. Error = Teacher. Copy it to Google, you’ll find the fix. What mistake did YOU make when you started? Tell me in the comments 👇

A few days ago I published a post about the three-layer auth model and the invoice incident that made...

There is a comparison that the artificial intelligence industry had kept out of the public conversation until now. Satya Nadella brought it up this Sunday in a post on X that garnered over a thousand responses in just a few hours. The metaphor he used is "industrial offshoring" . Just as the first wave of globalization hollowed out industrial economies, wiping out factory jobs and decades of competitive advantage with consequences we still feel today, artificial intelligence threatens to do the same to corporate knowledge. The Silent Drain of Expertise The mechanism Nadella describes is concrete. If an organization hands over its workflows, its domain knowledge, and the accumulated judgment of its teams to external AI models, those models absorb it. What was once a unique advantage, now could become a generic capability available to everyone. There are no layoffs or plant closures. The hollowing out happens silently, in every usage cycle, in every operation the model records and leverages. Where there was once exclusive know-how, there is now a standard resource. "You can outsource a task, or even a job. But you can never outsource the learning." Microsoft CEO Warns That AI Winners Could Hollow 'Entire Industries' - Business Insider AI models are hoovering up corporate knowledge, and that's leaving one big loser, says Satya Nadella. businessinsider.com The Invisible Asset / The Identity This warning is not directed at employees but at executives. The risk Nadella identifies is not the loss of an individual position, but the erosion of the organisation's collective intellectual property, its processes, and the judgment a team spends years building. To name this, he introduces a term that was not in the business management vocabulary until now: "Token Capital" . This represents the layer of agentic capability that a firm builds and owns when it connects its real workflows with the AI models it uses. It is not software or a database. It is a system that learns with eve

Outro dia, vi nos stories do Instagram uma amiga pesquisadora contando que estava usando o Claude para ressuscitar uma plataforma criada anos antes por parceiros. Ela não é da área de desenvolvimento de software. O projeto estava praticamente parado. Não havia mais recurso para manter tudo como estava. Com a ajuda da IA generativa, ela conseguiu migrar serviços, reduzir custo, melhorar performance, redesenhar partes da experiência e voltar a implementar coisas que estavam no backlog havia muito tempo. Achei aquilo inspirador! Mas também um pouco assustador. Não por ela estar usando IA generativa. Pelo contrário: acho fascinante que pessoas que não programam profissionalmente estejam conseguindo recuperar autonomia sobre projetos que antes ficavam dependentes de verba, disponibilidade de terceiros ou uma fila infinita de prioridades. O ponto que me acendeu uma luz amarela foi outro: em certo momento da conversa ela comentou que o projeto tinha dados de usuários e pagamentos via Stripe . Antes de seguir, uma ressalva: eu não gosto muito do termo "vibe coding" . Vou usar o termo aqui porque ele pegou, e porque todo mundo entende mais ou menos o que ele quer dizer: criar software com muita ajuda de IA generativa, muitas vezes sem dominar profundamente a linguagem, o framework ou a arquitetura por trás do projeto. Mas o termo me incomoda porque parece diminuir a responsabilidade envolvida. Se você está criando código, alterando código e colocando esse código no ar, você é sim uma pessoa desenvolvedora. Talvez iniciante. Talvez insegura. Talvez dependente demais da IA generativa. Talvez uma pessoa desenvolvedora ruim ou medíocre, como todos nós somos em algum recorte. Mas é. E isso traz responsabilidades. Vibe coding em uma página pessoal é uma coisa. Vibe coding em um sistema com login, dados pessoais, áreas administrativas, arquivos, integrações externas ou pagamento é outra. E aqui existe uma tensão interessante. Eu trabalho com desenvolvimento de software há bastante

By spring 2026, the division of labor between human engineers and AI had become precise enough to describe. Not speculate about. Describe. Delegate these 7 immediately: Boilerplate generation: CRUD scaffolding, config files, standard patterns. Near-human accuracy. Review required is a naming scan, not a logic audit. Test generation: 40-60% faster test development with no measurable decline in coverage quality, provided the tests are reviewed by someone who understands the domain. Documentation: 67% of companies rely on AI-assisted doc generation in 2026. The first draft is a solved problem. Your job is verifying and contextualizing. Code translation: Python to TypeScript. React to Vue. Framework migrations that once consumed sprint cycles now take hours. Routine bug fixing: Claude Code, Devin, BugBot can resolve 60% of reported bugs autonomously. Resolution time down 30-50%. Automated code review: First-pass filter before human review. Misses context issues. Doesn't replace human review. Eliminates noise so you focus on signal. Commit hygiene: Messages, PR summaries, changelog entries. Fully automatable. No meaningful error rate. Never delegate these 6: Architecture and system design: AI proposes. You decide. The tradeoffs require organizational context, team capability assessment, and long-horizon thinking no model possesses. Business context translation: The spec says "export to CSV." You ask: which users, under what conditions, with what compliance implications? AI cannot know the specification is wrong. You can. Security architecture: AI generates vulnerabilities as readily as it detects them. Adversarial thinking is not statistical. It is human. Long-horizon product thinking: What to build and why. Not how. Multi-stakeholder navigation: The politics, the relationships, the conversation with the PM that keeps the sprint on track. No model has stakes in the outcome. Agent orchestration: Designing, managing, and correcting the AI systems themselves. This is the ne

Something feels different about security incidents lately. Breaches, leaks, account takeovers, phishing campaigns they're not new. But their frequency, sophistication, and scale seem to be growing at a pace that feels genuinely alarming. Instagram accounts hacked overnight. Corporate systems compromised in hours. Phishing emails that sound disturbingly human. As someone studying AI & Big Data, I can't help but ask: is AI responsible for this? And if so, how? I think the honest answer is: yes but in two very different ways. The two faces of AI in cybersecurity When we talk about AI and cyberattacks, most people imagine one scenario: hackers using AI to attack systems faster and smarter. That's real. But it's only half the picture. The other half is something we talk about far less: the vulnerabilities that come from integrating AI into systems in the first place. These are two very different problems. And conflating them leads to the wrong solutions. Problem 1: AI is expanding the attack surface Every time a platform integrates an AI feature, they're adding something new to their infrastructure. And new infrastructure means new potential vulnerabilities. AI systems require: Massive data pipelines more data flowing through more systems APIs connecting multiple services more endpoints that can be exploited Third-party models and tools more external dependencies, more trust relationships Real-time processing less time to detect anomalies before damage is done Many organizations are integrating AI features faster than their security teams can audit them. And the consequences are already visible. In June 2026 , hackers reportedly manipulated AI-powered support systems to gain unauthorized access to Instagram accounts. The attack didn't target traditional software vulnerabilities it targeted the AI system itself , exploiting the automated account recovery flow that Meta had built with AI. This is the new reality: attackers are no longer just targeting your code. They're ta

Last quarter our OpenAI bill went from $620 to $2,480 in 23 days. No new features shipped. No traffic spike. Zero error alerts. Deployment logs were clean. Just a number climbing in silence while five engineers stared at dashboards that gave us totals and nothing else. This is what we found. And why "cost monitoring" is completely the wrong mental model. The dashboard that answers the wrong question First thing I did was open the OpenAI usage dashboard. It showed me a total. A graph going up. A model breakdown. I knew we spent $2,480. I still had no idea which feature spent it, which service triggered it, or which user was responsible. The dashboard was answering "how much" while we were desperately asking "what caused it." Those are completely different questions. Almost every cost tool on the market only answers the first one. That distinction matters more than most engineering teams realise until they are staring at a bill like ours. Three features, zero visibility We had three features hitting GPT-4o: A document summariser, triggered manually by users An inline suggestion engine, triggered on keystrokes A batch report generator, triggered on export Any one of them could be the problem. Or all three. Or one specific tenant hammering one endpoint in a loop nobody noticed. Without attribution at the feature, service, and user level, we were just guessing. So I did what most engineers do: optimised the feature that felt most expensive. Added caching to the one that ran most often. Two weeks later the bill was still climbing. Guessing at cost problems without attribution data is exactly like debugging a performance issue without a profiler. You move things around and hope. 48 hours of real data A teammate dropped CostReveal in our Slack. I set it up that evening. The Node.js SDK wraps your existing provider calls. You instrument each one with a feature name, service context, and user or tenant ID. That is the entire integration for the base case: import { CostReveal

Stop letting the prompt be your state machine You shipped an LLM feature six months ago. Now the same user input produces wildly different outputs depending on... nothing you can point to. Something in the sampling? The time the context filled up and a chunk got dropped? Nobody knows. This is what happens when the prompt becomes your runtime. The trap: the prompt as an accidental runtime Here is what the trap looks like in TypeScript: async function handleUserRequest ( input : string ): Promise < string > { const prompt = ` You are a helpful assistant. The user said: ${ input } Previous context: ${ someGlobalContext } Decide what to do, gather any information you need, format the response, and return it. ` ; return llm . complete ( prompt ); } The model is doing everything here: deciding the intent, gathering data, formatting output, choosing what to persist. That is a footgun. You handed the runtime to a stochastic function. Gartner attributes many failed agentic AI projects to unclear value and inadequate risk controls. Deterministic, testable workflows address both. The fix is not a better prompt. The fix is to stop using the prompt as an architecture. What "deterministic" can and cannot mean here Be honest about what you can and cannot control. You cannot control: the model's exact output. It is probabilistic by design. You can control: The shape of the output (structured output plus schema validation) The steps that run before and after the model call What data enters the model What happens when the output fails validation Whether a human reviews the result before it commits to anything irreversible Determinism here means: the same inputs, the same workflow steps, the same guardrails every time. Not the same tokens every time. That is a realistic and achievable target. It is also the thing teams skip when they are moving fast. Typed workflow steps around the model call Break the work into discrete typed steps. Each step has a clear input type and a clear output

What Recruiters Can't See On My GitHub If you spend about 30 seconds looking at my GitHub profile, you might think I'm all over the place. React. Python. Healthcare. AI. Scrapers. Automation. Marketing tools. Job bots. Honestly, that's something I've worried about. I have over 100 repositories. Recruiters can see most of them, but not all of them. Some are private because they're client work. Some are private because they're unfinished. Some are private because they contain ideas I've spent years developing and I'm not quite ready to throw the blueprints onto the internet. From the outside, it can look random. But recently I realized something. All of those projects are solving the same problem. I hate repetitive work. My GitHub is here: https://github.com/ashb4 The Job Application That Broke Me I've applied to thousands of jobs over the years. Thousands. And one thing has always driven me absolutely insane. You upload your resume. Then the company immediately asks you to type your entire resume into fifteen different boxes. Your work history. Your education. Your skills. Everything. The computer already has the information. The resume is right there. Yet somehow I'm sitting on page seven of an application retyping information that already exists. It feels inefficient. It feels stupid. And most of all, it feels like a waste of time. Eventually I got annoyed enough to start building tools to help. Then I Noticed a Pattern At first I thought I was building unrelated projects. A job application helper. A content scheduler. A healthcare platform. An AI framework. A browser automation system. But when I stepped back, I noticed the same motivation behind almost all of them. Every project started with some version of: "There has to be a better way to do this." Take PostPunk. Most people see a social media scheduler. I see hours of repetitive posting that I never want to do again. I like creating content. I do not like manually posting the same content everywhere. So I buil

Last year my side project was running on AWS. A t3.small EC2 instance, an RDS PostgreSQL db.t3.micro, an S3 bucket, and a CloudFront distribution. Total bill: $47/month for an app with 200 daily users. Then someone on Reddit told me to look at Hetzner. I now run the same stack on a single CAX21 (4 vCPU ARM, 8GB RAM, 80GB SSD) for €5.49/month. Here's exactly what happened. The Migration What I was running on AWS: Node.js API (Express) PostgreSQL database Redis for sessions Nginx reverse proxy Static files on S3 + CloudFront What I moved to Hetzner: Same Node.js API PostgreSQL installed directly on the server Redis installed directly on the server Nginx + Certbot for SSL Static files served by Nginx Total migration time: one Saturday afternoon. The hardest part was setting up automated backups (solved with a cron job + Hetzner's snapshot API). What Broke Nothing critical, but: No managed database failover. On RDS, if the database crashes, AWS restarts it automatically. On Hetzner, if PostgreSQL crashes at 3 AM, I'm the one fixing it. In 8 months, this has happened zero times. But it could. No CDN by default. My static assets now serve from a single Hetzner datacenter in Germany. For my EU-heavy userbase, this is actually faster than CloudFront. For US users, it's about 50ms slower. I added Cloudflare (free tier) in front and the problem disappeared. Deployment changed. No more eb deploy or push-to-deploy. I wrote a 12-line bash script that SSHs in, pulls from git, runs migrations, and restarts PM2. Takes 8 seconds. Honestly prefer it — I know exactly what's happening. The Cost Comparison at Every Scale This is what surprised me most. The gap isn't just at my small scale — it gets wider as you grow: SpecAWSDigitalOceanVultrHetzner2 vCPU, 4GB$30/mo$24/mo$24/mo€4.50/mo4 vCPU, 8GB$61/mo$48/mo$48/mo€8.50/mo8 vCPU, 16GB$122/mo$96/mo$96/mo€16/mo Hetzner is roughly 5-7x cheaper than AWS at every tier. DigitalOcean and Vultr sit in the middle. 👉 Calculate your exact costs When

Here is a problem I hit building a long-running agent: I needed to inject a new instruction partway through a session ("the project is Go, write Go") but editing the top-level system prompt to add it invalidated my entire prompt cache. Every cached turn got reprocessed at full price. The fix is a feature that landed in the current Claude models: mid-conversation system messages. Here is what it is and when to use it. The setup that breaks A long agent session has a large, stable system prompt and a growing message history, and you cache the prefix so each turn reuses the prior work cheaply. That works until you learn something mid-session that the agent needs to know: a mode toggled, the user delivered async context, files changed on disk, the token budget dropped. The naive move is to edit the system prompt to include the new fact. But the system prompt sits at the front of the cached prefix. Change one byte there and you invalidate everything after it. Your whole conversation history reprocesses at full input price on the next request. For a long session, that is expensive and slow. The fix: a system message in the messages array The current models let you put a system -role message directly in the messages array, after the history, instead of editing the top-level system : const response = await client . messages . create ( { model : " claude-opus-4-8 " , max_tokens : 16000 , system : [ { type : " text " , text : STABLE_SYSTEM , cache_control : { type : " ephemeral " } }, ], messages : [ ... history , // cached prefix, untouched { role : " user " , content : latestUserMessage }, // @ts-expect-error: role:"system" SDK types may still be landing { role : " system " , content : " This project is Go. Write all code in Go. " }, ], }, { headers : { " anthropic-beta " : " mid-conversation-system-2026-04-07 " } }, ); Because the new instruction sits after the cached history, it invalidates nothing before it. The cached prefix stays intact, you pay full price only for the

Every time I handed my Aadhaar card to someone for KYC, one thought kept nagging me: Where is this data actually going? Most "digital Aadhaar verification" tools out there silently upload your card details to their servers. You have zero visibility into what gets logged, stored, or sold. For something as sensitive as a national biometric ID, that's a pretty terrible default. So I built AadhaarQRCodeReader — a web app that scans the Secure QR on any Aadhaar card, decodes all the identity details, and does the entire thing inside your browser . No backend. No API calls. No data leaves your device. Ever. PtPrashantTripathi / AadhaarQRCodeReader 🇮🇳 Offline Aadhaar QR Reader — scan or upload any Aadhaar card, no server, no data leak. 🇮🇳 Aadhaar QR Code Reader Scan the Secure QR on any Aadhaar card to instantly verify identity details — 100 % offline, no server, no data leaves your device. ✨ Features Feature Details 📷 Live camera scan Uses the rear camera on mobile, front on desktop 🖼️ Image upload Pick any photo containing an Aadhaar QR from your gallery 🔒 100 % offline All decoding happens in the browser — zero network requests 🪪 Full card details Name, DOB, gender, address, mobile last-4, email (if present), issue date 🃏 3D card flip Front (personal) ↔ Back (address) card flip animation 🔗 Shareable URL Result is encoded in ?data= so links can be bookmarked 📱 Mobile-first Works on iOS Safari, Android Chrome, and desktop browsers 📸 Screenshots Scanner Verified Result (Front) Verified Result (Back) Point camera at any Aadhaar QR Personal details on the front face Address & reference date on … View on GitHub 🤔 Wait, what even is the Aadhaar Secure QR? UIDAI added a Secure QR Code to modern Aadhaar cards (and letters) — it's that big QR, not the small one. It's essentially a compressed, binary-encoded snapshot of your Aadhaar record containing: Name, DOB, gender Full address (house no., street, locality, district, state, PIN) Last 4 digits of your linked mobile Email (if yo

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

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

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