标签：#NeXT

🚀 Shipping a major update to my portfolio After weeks of designing, developing, and refining, I'm excited to share the latest version of my personal portfolio. Rather than building another static portfolio, I wanted to treat it like a real product—focusing on performance, interaction design, accessibility, analytics, and user experience. 🌐 Live: https://www.alanbabychan.online What I built 🟩 Matrix Theme A cyberpunk-inspired dark mode featuring animated binary effects, glowing UI elements, and an immersive developer experience. ⬜ Paper Theme A clean, modern light mode designed with readability, visual hierarchy, and clarity in mind. 🎵 Interactive Audio System •Background music •UI sound effects •Dedicated settings panel •Adjustable volume controls •Built using the Web Audio API 🖱️ Interactive Cursor A custom mouse-follow glow and subtle cursor interactions that enhance the browsing experience without becoming distracting. ✨ Micro-interactions Hover states, smooth page transitions, animated UI components, and responsive visual feedback to make every interaction feel intentional. 📖 UX & Accessibility Built around clear typography, intuitive navigation, responsive layouts, and accessibility-focused design to provide a consistent experience across devices. 📊 Performance & Analytics Built with Next.js and optimized for speed, SEO, and scalability. Implemented a complete Google Analytics 4 setup including: •SPA page tracking •Google Consent Mode v2 •Custom event tracking •User interaction analytics Tech Stack: Next.js • React • Tailwind CSS • Framer Motion • Web Audio API • Google Analytics 4 • Microsoft Clarity Coming Soon... 👀 I'm currently building a personal AI assistant that will allow visitors to interact with my portfolio, ask questions about my projects, experience, and skills, and explore everything conversationally. What I learned This project pushed me to dive deeper into: •Theme architecture •Frontend performance optimization •Animation systems •Custom UI inte

`California's AB 723 has been in force since January 1, 2026. It amends Business & Professions Code § 10140.8 and it applies to any real estate listing image that has been digitally altered. Virtual staging is the obvious case, but the definition is wider than that. The rule has two parts: A statement that the image has been altered, "reasonably conspicuous" and placed on or adjacent to the image. A link to a publicly accessible URL, or a QR code, that includes and clearly identifies the original, unaltered image. Everyone builds the first part. It is a text label on a photo, an afternoon of work. The second part is a small piece of infrastructure: a permanent public URL, per image, that outlives the tab the agent had open when they exported. I build a virtual staging product, so I had to ship both. This is how the second part is put together, and the one thing I got wrong. What counts as altered Worth getting right before writing any code, because it decides which of your features need the label and which do not. Subsection (b)(2) carves out ordinary photo editing. Covered: Adding furniture, rugs, art or decor Removing furniture, clutter or personal items Changing paint, flooring or wall finishes Sky replacement and day to dusk Greening or reshaping lawns and landscaping Anything that changes the facade or the property itself Not covered: Exposure, lighting, white balance, color correction Sharpening Straightening, cropping, angle In the codebase that line is a set, and the two omissions are deliberate: ts export const TOOLS_ALTERING_LISTING_IMAGES = new Set([ "virtual-staging", "sky-replacement", "day-to-dusk", "grass-greener", "declutter", "object-remover", ]); image-enhancer is out because exposure and white balance are precisely what the statute excludes. A floor plan generator is out because a diagram is not an altered photograph. Attaching a legal claim to a feature the law does not cover is not a harmless extra: it is the fastest way to make the rest of your

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. Way back in the summer of 2017, AI researchers at Google put out a paper called “Attention Is All You Need,” in which they described a new…

Originally published on tamiz.pro . Caching in modern web development is no longer just about serving static assets faster; it is the primary mechanism for balancing performance, cost, and data freshness. In the context of Next.js, the caching architecture has evolved significantly, shifting from a simple getStaticProps / getServerSideProps dichotomy to a sophisticated, multi-layered system that spans the Edge Runtime, the Server Components architecture, and the Node.js server environment. For software engineers and systems architects, understanding the default behaviors of Next.js caching is insufficient. To build production-grade applications that handle high concurrency without hammering your database, you must master the advanced patterns: granular revalidation, cache tagging, and external cache management. This article dives deep into these mechanisms, explaining how they work under the hood and how to orchestrate them for optimal performance. The Evolution of Next.js Caching To appreciate advanced patterns, we must first contextualize the current caching model. Next.js 13+ (App Router) introduced a new caching paradigm that is both simpler by default and more powerful when customized. The default behavior is now: App Router (RSC): Components are cached by default. Server Components are rendered once and cached on the server. The next request for the same data returns the cached result. Static Generation: Pages and layouts are built at build time and served statically. Server Components: Fetched data is cached in memory on the server, not in the browser. The critical shift here is that caching is opt-out, not opt-in . Previously, you had to explicitly mark things as static. Now, you must explicitly invalidate cache when data changes. This inversion of control places the responsibility of consistency squarely on the developer, requiring precise tools to manage invalidation. Granular Revalidation: The Tag-Based System The most significant advanced caching pattern

There is a quiet moment of panic every developer knows. You hit deploy, open the live site on your phone, and wait. One second. Two seconds. Four seconds. Still a blank white screen. A while back, I was working on a Next JS application that looked fast on high speed office Wi Fi. But when tested on a spotty mobile connection, it felt painfully slow. The initial page load was clocking in at nearly 8 seconds, and our main JavaScript bundle was a bloated 1.8 megabytes. Here is how we diagnosed the bloat, cut our load times by 47 percent, and the simple performance rules every developer should know. The Investigation: Where Was the Weight Coming From? When a website is slow, our first instinct is often to blame slow backend APIs or heavy database queries. But when I ran a performance audit, the backend was not the problem at all. The front door was just jammed with too much stuff. We were making three classic mistakes: First, we were packing for a long trip on a short walk. We were loading heavy charting libraries, complex admin tables, and pop up modals the second a user landed on the home page, even if that user only came to read a single line of text. Second, giant images were being served to tiny mobile screens, hogging precious bandwidth before any interactive buttons could even load. Third, a single state update at the top of our app was causing dozens of unseen child components to recalculate and re render unnecessarily behind the scenes. The Strategy: Trimming the Fat Instead of rewriting the entire codebase from scratch, we focused on three targeted fixes. 1. Don't Load It Until They Ask For It Why force a user to download a complex analytics chart if they have not even clicked on the dashboard tab yet? We split the app into smaller, independent code chunks. Now, the user downloads only the absolute bare minimum needed to view the immediate screen. The heavy features stay on the server until the exact moment the user interacts with them. 2. Smart Asset Delivery

AI tool reviews rot faster than anyone can rewrite them. A tool that was S-tier in March ships a broken pricing change in June, a "top 10" listicle from last year recommends products that no longer exist, and every directory slowly turns into a graveyard of dead links. I run AI Tier List , a bilingual (EN/KO) directory that ranks 245+ AI tools from S to D. My answer to review rot: don't re-review by hand. Make a pipeline re-rate everything weekly, and let humans only approve or reject. The architecture Everything runs on one weekly GitHub Actions cron (Next.js 16 + Prisma + Neon Postgres + Vercel): weekly cron (Sun 00:00 UTC) ├─ collect Google Trends per tool → trend scores ├─ collect OpenRouter usage rankings → weekly LLM leaderboard ├─ deactivate dead tools → site checks + trend slump ├─ discover new tools → search + AI triage ├─ re-evaluate tiers (LLM) → PendingUpdate rows └─ generate weekly blog draft → MDX The key design decision: the LLM never writes directly to the live site. Re-evaluations land in an approval queue ( PendingUpdate table). I review diffs in an admin panel and approve batches. The pipeline proposes; a human disposes. That one boundary is what keeps automated content from becoming automated garbage. Two collectors do the heavy lifting: Trend collector — Google Trends per tool, weekly. A tool in a sustained slump gets flagged; if its website also starts failing health checks, it gets deactivated automatically. Dead products remove themselves from the directory. OpenRouter collector — real token-usage data powers a weekly LLM leaderboard . No opinions, just "which models did people actually route traffic to this week," with usage share, pricing, and context length. What the tier actually means Each tool stores bilingual tierReason , strengths , and weakness fields, and the tier maps S→5 … D→1 into review schema markup. When the weekly re-evaluation moves a tool, the reason is regenerated with it — so the rating and its justification never drift a

This article is an English translation of the original Japanese article. When I added an Expo app to an existing Next.js web service, I initially wanted to share as much code as possible. In practice, types and business rules share well, while UI and runtime dependencies are easier to manage separately. SquadNote uses pnpm workspace and Turborepo, composed of apps/web , apps/mobile , and packages/* . Current split apps/ web/ Next.js, Cloudflare Workers mobile/ Expo, React Native packages/ api/ tRPC and Zod shared parts db/ Drizzle schema design-tokens/ Root workspace configuration is simple: packages : - " apps/*" - " packages/*" turbo.json manages only build and typecheck dependencies. Rather than adding custom build steps for sharing, I started with a structure where each package exports TypeScript sources. What I share The biggest benefit came from tRPC types. Mobile type-imports the web AppRouter , using the same input and output. import type { AppRouter } from " ../../apps/web/src/server/api/root " ; export const api = createTRPCReact < AppRouter > (); I also separated colors, spacing, and font sizes into @squadnote/design-tokens . export { colors } from " ./colors " ; export { spacing } from " ./spacing " ; export { radius } from " ./radius " ; Business rules like waitlists become sharing candidates as pure functions with no dependency on React or DB. They are easy to test, and results do not diverge between web and mobile. What I do not share I do not share screen components. Next.js DOM and React Native View have different interactions, accessibility, and layout constraints, even when they look similar. Authentication storage also differs: Web: NextAuth cookie session Mobile: SecureStore Bearer JWT Both reach the same API, but sharing the login screen and token storage would require handling each environment's concerns with many branches. Routing also has separate implementations for Next.js App Router and Expo Router. What I share is the meaning of organiza

A short explanation of a wall a lot of developers hit, why it isn't going away, and the five lines that replace it. You wrote a contact form. It worked locally. You deployed it to a Cloudflare Worker, or a Vercel Edge Function, or Deno Deploy, and got something like this: TypeError: Class extends value #<Object> is not a constructor or null Or, if you were luckier and got a useful error: Module not found: Can't resolve 'net' Then you spent an hour trying compatibility flags, polyfills, and bundler aliases. I want to save you the rest of that hour. This isn't a bug, and no amount of configuration will fix it. The actual reason Nodemailer's default transport is SMTP. SMTP is a protocol that runs over a raw TCP connection. To open one in Node.js, you call net.createConnection() . Cloudflare Workers don't run on Node.js. They run on V8 isolates — the same engine as Chrome, without the Node runtime around it. Vercel's Edge Runtime and Deno Deploy are built on similar principles. In that environment, there is no net module, because there are no raw TCP sockets. All networking is handled by managed infrastructure outside the runtime — Cloudflare's own writeup on bringing node:http to Workers is explicit about this: connection pooling, TLS negotiation, and egress IP management are handled at the system level, which is precisely why a subset of Node APIs can never be supported. So the chain is: No raw TCP → no net.createConnection() → no SMTP client → no Nodemailer. There's a second, smaller issue that often gets conflated with this one. Nodemailer issue #1621 points out that Nodemailer imports built-in modules without the node: prefix, which breaks the Workers build step. That one is fixable. But fixing it wouldn't help — you'd just move the failure from build time to runtime, where net still doesn't exist. Issue #1623 covers the broader edge-function problem. It's worth being clear that none of this is a knock on Nodemailer. It's an excellent library, actively maintained,

If you're actively applying for jobs, you probably know the struggle: Did I already apply to this company? Which resume version did I send? What salary did I mention when I applied? What was the budget mentioned in the job posting? When did I apply for this one? Which interviews are scheduled this week? What were the HR contact details again? What exactly were the requirements for this role? When is my next interview? Where did I even find this posting? How many of my applications are actually turning into interviews? Every one of those is answerable. The problem is that the answers are scattered across a spreadsheet, a notes app, your inbox, and your memory — and reassembling them takes longer than the follow-up you were trying to send. Spreadsheets are where most people start, and they hold up until somewhere around application number twelve. After that, searching, filtering, and keeping the thing current becomes its own small job — and a spreadsheet still won't tell you that six applications have been sitting in "Applied" for a month, or whether your last twenty went better than the twenty before them. That's why I built HireLoop — an advanced job application tracker meant to reduce the mental load of a job search rather than add to it. Live app: hireloop.yogeshchavan.dev — free to use, with a demo account if you'd rather look around before signing in. Check out the application demo video below: Check out some preview images of the application The short version With HireLoop you can: Track every application in one place — status, dates, salary, source, and links See where your search stands at a glance on a dashboard Move applications through a Kanban pipeline Search, filter, and sort as the list grows See interviews and deadlines on a calendar Analyse interview rates, offer rates, application trends, and which sources actually work Store notes, resume versions, HR contacts, salary details, and job links per application Mark the ones that matter as favourites Kee

With the help of Claude Code, I finished a task that I had pushed aside for years in two days: To move away from WordPress for my blog . 1. Background WordPress had served well between 2016 and 2019, when I was still learning how to write apps with a framework like Ember. Over time, however, the annual cost of $48 (plus $28 for the domain, excluding taxes) felt overpriced, given the lack of features (e.g. no syntax highlighting for *.{gjs,gts} ) and many paywalls for customization. Reposting a blog post on dev.to was also tedious, since I need to write the content on WordPress using a proprietary, interactive editor, while in Markdown on dev.to. I would copy the output text from WordPress, then convert the output to Markdown. I finally had enough when WordPress broke the styles for code blocks again : Once after I had migrated from the classic editor to the current one, and the second time recently while playing with the admin dashboard. 2. Move to Next.js I decided to rebuild my blog in Next.js, a framework suited for blogs and in demand. The app is to be deployed on Netlify, and the domain stays with WordPress through a DNS configuration. I saw the opportunity to use Claude Code for the first time, as I had little experience with Next.js and wanted to see how far I can get with unknown technologies in two days. Thanks to prior experience in blogging on different platforms, I had a good idea of how to store blog content and metadata ( front matter ) in a Markdown file and what users should be able to do when they visit my blog. I also studied the current URLs so that (1) I can tell Claude how to structure the project in Next.js and (2) URLs won't be broken after the migration. What I knew would take the most time and delegated to Claude Code: Create components and routes to provide a similar functionality. Generate Markdown files for blog posts that I didn't repost on dev.to. Many of these were related to math and engineering and included LaTeX in inline and block

Building the layout orchestrator for Linkscribe wasn't a simple case of slapping a pre-made library onto a list. It was an ambitious attempt to construct a hybrid rendering stack—wrapping React Virtualized, delegating observer callbacks, and orchestrating DOM updates across dynamic multi-column folders. Having built virtualization engines completely from scratch before—ranging from off-thread Web Worker layout calculators to adaptive sync engines using relative spatial rendering—I approached this with a specific theoretical model in mind. Calling this an orchestrator or a custom engine fits what it was designed to do. However, my initial mental model fell apart when real-world DOM mutations, multi-column sections, and rapid reload cycles collapsed the execution pipeline. Testing 200 items in nested folders dropped the frame rate to ~20 FPS during fast reloads and rapid scrolling. The orchestration overhead simply choked the main thread. Problem 1: DOM Event Saturation and Thread Blocking The core bottleneck came down to how the delegation layer managed element state changes. Connecting MutationObservers and IntersectionObservers directly to global store triggers filled the browser event queue with continuous updates. The Old Approach The delegation manager listened for node insertions across the DOM tree and triggered immediate state changes on every single intersection callback. observerRef . current = new IntersectionObserver (( entries ) => { entries . forEach ( entry => { // Continuous individual state calls during rapid layout shifts hydrateActiveMonoLink ( id ); }); }); Why this broke down During rapid scrolling or fast view reloads, dozens of elements entered and left the viewport simultaneously. Processing these events individually saturated the main thread, forcing constant DOM querying and component re-evaluations while the browser was trying to handle paint cycles. The Refactored Direction Consolidating intersection calculations into unified updates preve

Two customers are looking at the same product. One unit left. Within the same second, both click Pay. If your checkout reads the stock count, decides there's enough, and then writes the decrement, both requests pass the check and both succeed. You've now sold two units of something you had one of. That's overselling, and it's not a rare edge case — it's the default behaviour of any checkout that treats "check stock" and "reduce stock" as two separate steps. The window is small, but on a product that's nearly sold out, or during a launch when everyone hits the same SKU at once, small windows fire constantly. I've built the order pipeline for two production e-commerce platforms — pikkuna.fi and pi-pi.ee — where concurrent webhooks and concurrent checkouts hit the same order and product rows. This is the layer I reach for when a store sells finite stock. I covered the bare SELECT ... FOR UPDATE primitive briefly in PostgreSQL Production Patterns ; this article is the whole system built on top of it — reservations, multi-line carts, the payment window, and the parts that actually bite you in production. When You Don't Need Any of This Start with the honest disclaimer, because it decides everything downstream. Both pikkuna.fi and pi-pi.ee are made-to-order . A vinyl curtain is cut to the customer's dimensions; a waterless urinal system ships from a supply chain, not a shelf with a hard unit count. When there's no fixed quantity to run out of, overselling isn't a failure mode — you can't sell the tenth unit of something you manufacture on demand. So neither of those platforms needs a row lock on a stock column, and I didn't build one there. You need this article when you sell discrete, finite stock : limited runs, event tickets, one-off items, anything where "5 left" is a real number and selling the sixth is a promise you can't keep. If your catalogue is print-on-demand, made-to-order, or backed by effectively unlimited supply, stop here — the locking below is complexity

Photo-to-cross-stitch conversion looks like a resizing problem. It is not. A pixelated preview can look convincing and still be frustrating to stitch. It may contain too many colors, lack readable symbols, provide no reliable dimensions, or become useless when printed. I built StitchFromPhoto to handle the practical part of that workflow. It turns an image into a counted cross-stitch chart in the browser, lets you tune the result before committing to it, and keeps the source photo on your device. The useful output is a pattern, not a pixelated image A cross-stitch preview only answers one question. It shows roughly what the finished piece might look like. A usable pattern must also tell you how many stitches wide and tall the design is, which thread color belongs in each square, whether similar colors remain distinguishable on paper, and how large the result will be on your chosen fabric. That distinction shaped the app. The color preview is useful, but the symbol chart, thread key, stitch totals, fabric dimensions, and printable pages are the real deliverables. What the photo-to-cross-stitch pattern maker does The workflow starts with a sample image, so anyone can explore the controls before uploading a file. It also accepts JPG, PNG, and WebP images up to 20 MB. The main controls are stitch width, DMC color count, and fabric count. You can choose a pattern from 30 to 120 stitches wide, limit the palette to between 6 and 36 DMC colors, and calculate the finished size for 14, 16, 18, or 22 count Aida. You can move between the original photo, a color stitch preview, and a high-contrast symbol view. The thread key lists every retained DMC color code and the number of stitches assigned to it. Creating and previewing a pattern is free. High-resolution PNG and print-ready PDF downloads are unlocked per source image. I wanted that boundary to be visible before checkout rather than hidden behind the final button. How the browser turns pixels into stitches The conversion pi

There's a workflow that basically didn't exist three years ago and now half the job-seekers I know use it: ask ChatGPT, Claude, Gemini, or any AI to write your resume bullets, get back beautifully structured text… and then spend forty minutes mangling it into Word or a drag-and-drop resume builder, fixing bullet indentation and font sizes by hand. Here's the thing that bugged me: LLMs already speak Markdown. Ask any chatbot for a resume and you get ## Experience , **Senior Engineer** , - Shipped X — clean, structured Markdown. Then every resume tool on earth makes you throw that structure away and re-enter it into form fields. So I built ResumeMD: a split-pane editor where you paste Markdown on the left, see a typeset resume on the right, pick a template, and download a PDF. No signup to start, everything in localStorage by default. This post is about the parts that fought back. Decision 1: Markdown is the source of truth Most resume builders store your resume as a proprietary JSON blob mapped to form fields. I wanted the document itself to be portable text. That means the entire product is "just" a Markdown renderer with opinions: h2 = section headers (Experience, Education) — these get the decorative treatment per template: uppercase, border, background, prefix glyphs. h3 = job titles — plain, bold, primary color. One weird trick I'm genuinely fond of: the sidebar template splits a single Markdown document into main column and sidebar using an HTML comment ( <!-- sidebar --> ) as the split marker. Content above the marker is the main column; below is the sidebar. It keeps the document valid Markdown everywhere else. The preview is react-markdown + remark-gfm with a 300ms debounce, styled by a template system that turned out to need three parallel implementations of every template: CSS classes for the live preview, inline-style functions shared between preview and template cards, and pure-JS styles for the PDF renderer. Thirty-two templates, three layers each. When

Most AI image generators look like a prompt box with a Generate button. That is also how my first version started. But once real users entered the workflow, the difficult problems appeared somewhere else: browser refreshes, external task IDs, reference images, partial failures, credit refunds, private assets, and public artwork moderation. While building Magggic , I learned that an AI image generator is less like a form submission and more like a small distributed job system. This article covers the decisions that made that workflow more reliable. The code samples below are intentionally simplified. The important part is the shape of the workflow, not a specific database or image provider. The prompt box is only the beginning A synchronous prototype is easy to imagine: const images = await provider . generate ( prompt ); return images ; That version works until the request takes a minute, the provider times out, one of four requested images fails, or the user refreshes the page. The production workflow I needed looked more like this: Prompt + references ↓ Create a local queued task ↓ Charge credits with an idempotency key ↓ Submit work to the image provider ↓ Persist every completed output immediately ↓ Finalize the task and refund failed outputs ↓ Keep the result private until the user publishes it The provider request is only one step. The local task is the source of truth for what the user sees. 1. Persist the task before calling the provider The first important decision was to create a generation record before making the external API request. A generation stores the information needed to reconstruct the job: type Generation = { id : string ; userId : string ; idempotencyKey : string ; prompt : string ; referenceImages : string []; model : string ; ratio : string ; resolution : string ; count : number ; cost : number ; status : " queued " | " generating " | " completed " | " failed " ; outputs : string []; providerRequestIds : string []; failureReason : string |

LinkBreeze is a self-hosted alternative to Linktree. I built it because Linktree's $15/mo Pro plan didn't justify the feature set, email capture is another $9/mo, embed widgets are paywalled, link scheduling is paywalled. I wanted something I actually own: my data on my server, no subscription, no tracking pixels. The interesting technical bit: the public page ships zero client-side JavaScript. The entire link-in-bio page, themes, animations, hover effects, QR codes, embed widgets, renders server-side as pure HTML/CSS. No React runtime, no hydration, no framework JS. The visitor downloads HTML + CSS + their fonts. Page loads in under 300ms. That's it. Feature gap vs. the competition (what pushed me to build this): Feature Linktree LinkStack LittleLink Shako LinkBreeze Price $15/mo Free Free Free Free Admin Panel ✅ Slow ❌ ❌ ✅ Fast Multi-Page Paid ❌ ❌ ❌ ✅ Migration Wizard ❌ ❌ ❌ ❌ ✅ Built-in Analytics Paid Basic ❌ ❌ ✅ Full External Analytics ✅ ✅ ❌ ❌ ✅ Email Capture Paid ❌ ❌ ❌ ✅ Embed Widgets Paid ❌ ❌ ❌ ✅ Link Thumbnails Paid ❌ ❌ ❌ ✅ Link Scheduling Paid ❌ ❌ ❌ ✅ Themes Paid Limited CSS only Config ✅ Full Token System + Import/Export Custom CSS ❌ ❌ ✅ ❌ ✅ Language Closed PHP HTML Astro TypeScript Docker Deploy N/A Complex Simple Simple One command License Closed AGPL MIT GPL MIT Live demo (read-only): https://linkbreeze-demo.omnirise.dev/alex Admin demo: https://linkbreeze-demo.omnirise.dev/login (demo / demo1234) Repo: https://github.com/Manak-hash/LinkBreeze I'd genuinely appreciate feedback, bug reports, or feature suggestions. What's missing compared to what you'd expect from a self-hosted tool like this?

Bridging Design and Code to Empower Local Businesses As a full-stack developer specializing in JavaScript and React, one of the most exciting ventures I'm currently on is building ready-made websites and Next.js templates through Softchic. This isn't just about coding; it's about deeply understanding the needs of businesses, particularly within the vibrant and rapidly evolving Nigerian market, and translating those into high-performance, beautiful web solutions. Why Next.js? Performance, SEO, and Developer Experience My choice of Next.js as the primary framework for these templates was deliberate: Performance: Server-side rendering (SSR) and static site generation (SSG) capabilities are crucial. In areas where internet speeds might vary, a fast-loading website isn't just a nice-to-have; it's essential for user retention and conversion. SEO: For businesses looking to establish a strong online presence, robust SEO capabilities out-of-the-box mean our templates provide a solid foundation for discoverability. Developer Experience: Building with Next.js allows for efficient development, leveraging the power of React while simplifying routing, data fetching, and API routes. This means faster iteration and higher quality templates. The Nigerian Market: Unique Challenges, Immense Opportunity Crafting templates specifically for the Nigerian market presents a fascinating set of considerations: Design Aesthetics: Understanding local preferences in terms of color palettes, layouts, and user flows is critical. It's not just about what looks good globally, but what resonates locally. This is where my dual role as creative director for promotional materials comes into play – applying that eye for design directly to the templates. Mobile-First Mentality: A significant portion of internet users in Nigeria access the web via mobile devices. Every template is meticulously designed with a mobile-first approach to ensure optimal responsiveness and user experience on smaller screens. Aff

Static site generation has a branding problem. Say "static site" and people picture a blog with twelve posts and a contact form. So how far can you actually push it before you need a backend? Further than most people assume. This is a walkthrough of a production site that has no database, no API layer, no user accounts and no server-side state, and still ships 232 prerendered pages with per-user results, shareable links and dynamic social cards. The site is a Spanish political test with nine ideological axes, seventeen parties, fifty-four questions. It is in Spanish, but nothing here depends on reading it. Treat it as the reference implementation. The architecture in one sentence Three data files are the source of truth, everything else is derived at build time, and everything user-specific happens in the browser. That is the whole trick. The rest is consequences. 1. Derive pages, don't author them The site has 232 URLs. Almost none of them were written by hand. There are three data modules: the axes, the parties, and the questions. From those, generateStaticParams produces every content route: // app/ejes/[id]/page.tsx export function generateStaticParams () { return AXES . map (( a ) => ({ id : a . id })) } The interesting one is the comparison pages. Seventeen parties means 17 × 16 / 2 = 136 unique pairs, and each pair gets its own page, its own metadata and its own canonical URL: export function allPairs () { const out = [] for ( let i = 0 ; i < PARTIES . length ; i ++ ) for ( let j = i + 1 ; j < PARTIES . length ; j ++ ) out . push ({ a : PARTIES [ i ]. id , b : PARTIES [ j ]. id }) return out } export function generateStaticParams () { return allPairs (). map (( p ) => ({ pair : pairSlug ( p . a , p . b ) })) } 136 pages from twelve lines. And because the page body is computed from the same vectors, recalibrating one party silently rewrites the sixteen pages that involve it . No CMS, no migration, no content drift. The numbers on the page cannot disagree with

This is part of my Building Fluentic Style series, where I’m writing down the design decisions, tradeoffs, and small surprises from building Fluentic Style . It is one thing to make a styling library feel good in a client-side app. It is another thing to make it feel good in Next.js App Router. In a simple SPA-style development setup, most of the styling loop lives in one place: component renders in the browser Fluentic style chain resolves atomic CSS rule is inserted DevTools can inspect the generated rule sourcemap points back to authored code That is already a lot of work. But at least the browser is the main place where the style is produced and consumed. Next.js App Router changes the shape of the problem. Now the page can involve: server rendering React Server Components client components streamed HTML hydration client-side navigation HMR Webpack or Turbopack development sourcemaps production extraction So the hard part is not just “can Fluentic run in Next.js?” The hard part is: Can Fluentic keep the same CSS debugging experience when styles cross the server/client boundary? That is what this post is about. Docs for the Next.js integration are here: Next.js Integration DevTools And Sourcemaps Runtime And Dev Debug Without Getting Lost The Goal Was Not A Special Next.js API I did not want Fluentic to have one mental model for client apps and another one for Next.js. This should still be normal Fluentic: const card = style ({ padding : 16 , borderRadius : 12 , }). hover ({ boxShadow : ' 0 12px 30px rgb(15 23 42 / 0.16) ' , }); export function Card () { return < section css = { card } > Hello </ section >; } And this should still be normal Fluentic too: const buttonStyles = { root : style . slot ({ display : ' inline-flex ' , border : 0 , }), label : style . slot ({ fontWeight : 700 , }), }; const danger = style . scope ([ buttonStyles . root ({ backgroundColor : ' #dc2626 ' , }), buttonStyles . label ({ color : ' #ffffff ' , }), ]); The Next.js integration shou

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry . The build was done. Themis Lex worked on my machine, and not in the "works if you squint" way. A court clerk enters their role, describes their workflow, picks a data sensitivity level, and gets back a PDF with two sections: where AI can safely support the work, and where it must never touch it. Claude via Bedrock generates the assessment. Server-side PDF render. No accounts, no storage, session ends when the download does. Three weeks solo, for the Women in AI Accelerator Spring 2026 Build Challenge. Initial commit went in at 7:06pm on May 9. I pushed to AWS Amplify . Build went green. I opened the live site, filled out the form, hit submit. Nothing. Twenty eight seconds later, "Request timed out." I told myself the bug was not in my code. Everything ran locally. This had to be a platform problem. That belief carried me all night. It mostly held up. The exception was the first thing I should have checked. Here is the commit log, because it tells the story better than I can: 19:06 Initial commit: Themis Lex MVP 20:31 refactor: migrate Bedrock auth to IAM compute role 22:29 diag: log credential env vars at runtime (booleans only, remove after fix) 22:40 fix: forward BEDROCK_MODEL_ID to SSR runtime via next.config.js env 22:50 fix: switch to InvokeModelWithResponseStreamCommand to beat 28s Lambda timeout ... 06:28 fix: remove unused type export that broke isolatedModules build 06:37 fix: end-to-end response streaming to beat Amplify 28s gateway timeout 06:48 fix: reduce max_tokens to 3000 to fit Amplify 30s timeout 06:52 fix: reduce max_tokens to 2000, 3000 still exceeded 30s timeout 07:00 fix: switch to Claude Haiku 4.5 to fit Amplify 30s timeout Ten and a half hours from first deploy to the fix that shipped it. That gap between 22:50 and 06:28 is me sleeping on it, which turned out to be the second most productive thing I did. The error message was the absence of an error message My f