标签：#X

Every headless WordPress conversation starts the same way — someone draws an architecture diagram with arrows pointing from a REST API to a shiny Next.js frontend, and it looks clean. Too clean. This is a post about what happens when you close the whiteboard and open the actual codebase. The Stack Decision: GraphQL vs. REST vs. Direct MySQL This is usually the first fork in the road. For this build, the client already had a well-indexed WooCommerce site. The product catalog, slugs, and taxonomy structure were already doing heavy SEO work. So the constraint was simple: nothing about the data layer changes, only how we consume it. WPGraphQL was a real option — but it meant adding a plugin dependency to a WordPress install we were actively trying to slim down. The WP REST API was already there, no installation required, and exposed exactly what we needed: products, categories, pages, and media — all queryable by slug. The decision: WP REST API, consumed server-side via Next.js fetch in Server Components. // Fetching a product by slug — preserving the existing URL structure const res = await fetch ( ` ${ process . env . WP_API_BASE } /wp/v2/product?slug= ${ params . slug } &_embed` , { next : { revalidate : 3600 } } ); const [ product ] = await res . json (); No new dependencies on the WordPress side. The legacy install runs as a lean shell — no active theme, minimal plugins, just the REST API and the data. The Site Kit Problem: Bridging Familiar Workflows This is where most migrations quietly fail the client. The previous team lived inside WordPress admin. Google Site Kit gave them traffic stats, Search Console data, and Analytics — all surfaced in a UI they knew. Ripping that away and telling them "just use Google Analytics directly" is a workflow regression, not an upgrade. The pivot here was building a lightweight admin dashboard as part of the Next.js project — not a full replacement for Site Kit, but a mirror of the metrics they actually checked daily: Page views

Hey everyone! Our team and I (acting as a junior backend) recently finished rebuilding GlobeVM (an enterprise Cloud, IT, and Cybersecurity provider) from a traditional monolithic WordPress site into a Headless architecture. I wanted to share our entire journey, our tech stack, the massive headaches we faced, and the solutions we engineered. If you are planning a Headless WP build soon, grab a this might save you weeks of debugging! The Tech Stack Frontend: Next.js (App Router), React, deployed on Vercel. Backend: WordPress hosted on Cloudways (Purely as a headless CMS). Data Structure: Advanced Custom Fields (ACF Pro) + Custom Post Type UI (CPT UI). SEO: Yoast SEO Premium (via REST API). Caching: Vercel Edge Cache (ISR) + Redis Object Cache on Cloudways. During the development and deployment of this website we faced several issues containing the frontend stack itself and traditional features of WordPress. I tried my best to save them all and show them all up here for everyone so that we can discuss on every section of it, maybe we could reach to even something more special :) Challenge 1: The API Was a Mess ("BFF" Architecture) When we first started, we were using the default WordPress REST API. Our Next.js frontend was making 8 different fetch() calls just to build the Blog Homepage (fetching posts, authors, categories, tags, ACF fields, etc.). We were also using messy URLs like ?_fields=id,title,acf&_embed. The Solution: We built a Backend-For-Frontend (BFF). Instead of Next.js doing the heavy lifting, we wrote a custom PHP plugin in WordPress. We created a single master endpoint (/wp-json/gvm/v1/blog-home). WordPress ran all the complex database queries, bundled the Tags, Hero Article, and Categories into one beautiful JSON array, and cached it in RAM using Transients & Redis. Only one of WordPress default api was used for our categories. Result: Next.js made one fetch call. The page loads instantly.' Challenge 2: Handling Vector Icons & ACF Our design heavily re

Introduction In today's fast-paced business environments, data is considered the cornerstone of decision-making, policy formulation, and other organizational needs. MS Excel is a robust spreadsheet developed by Microsoft for organizing, analyzing, and visualizing data in rows and columns. In the data science and analytics domain, MS Excel is critical for analyzing and managing data to generate insights that enhance decision-making. Excel's polarity is characterized by its ease of use, flexibility, automation, and visualization. Ways Excel Is Used in Real-World Data Analysis Across the data science and analytics domain, MS Excel is frequently employed in the following ways; a) Data Cleaning and Preprocessing At the beginning of every data science and analytics project, data cleaning is required, and MS Excel is the primary tool. Typical Excel features and functions applied during data cleaning include Text to Columns, Remove Duplicates, Find and Replace, and Power Query. b) Exploratory Data Analysis Before performing data science and analytics activities, it is crucial to understand the dataset at hand, its structure, and trends. MS Excel features Pivot Tables, Pivot Charts, and Slicers that provide instant aggregation, sorting, and visualizations. c) Data Analysis and Reporting Modern organizations and businesses operate based on insights generated from data. MS Excel features such as pivot tables, charts, and conditional formatting help data analysts analyze and visualize data for clear, actionable insights that enhance decision-making. MS Excel Features or Formulas The typical MS Excel features and formulas employed in the data science and analytics domain include the following. Data Cleaning Functions Function Purpose Example Result UPPER() Converts text to uppercase =UPPER("john") JOHN LOWER() Converts text to lowercase =LOWER("JOHN") john PROPER() Capitalizes the first letter of each word =PROPER("john doe") John Doe TRIM() Removes extra spaces from text =TRIM(

In the last update, I introduced MDL as an HTML-first language for building websites and apps with less noise. This update is about the next layer: adapters . The goal is simple: MDL source should describe intent. Adapters decide how that intent becomes deployable HTML. Why adapters? I don’t want MDL to become locked into one frontend framework. Instead, the same MDL structure should be able to target different deployment styles: static HTML MDL-native runtime attributes HTMX future template or framework adapters So this: form@api(post /api/login)@result(loginResult)@swap(replace): .input@type(email)@required .btn-primary@type(submit)(Sign in) status@id(loginResult): Waiting. Can become plain HTML: html <form method= "post" action= "/api/login" > Or HTMX: html <form hx-post= "/api/login" hx-target= "#loginResult" hx-swap= "outerHTML" > Security before convenienceThe important part is that MDL does not pass behavior attributes through blindly. Raw HTMX attributes like this are blocked: mdl form@hx-post(/api/login): Instead, MDL uses intent-based attributes: mdl form@api(post /api/login): Then the adapter validates and translates it. Current safety rules include: external api(...) URLs are rejected raw @hx-* attributes are blocked raw browser events like onclick(...) are blocked unsafe URL schemes like javascript: are blocked broad form inclusion like @params( ) is blocked @inherit( ) is blocked @disinherit(*) is allowed because disabling inherited behavior is safer That means MDL can support HTMX without making MDL source depend directly on HTMX’s full raw surface area. New HTMX adapter attributesThe HTMX v2 adapter now supports more behavior attributes: mdl @select-oob(...) @swap-oob(...) @disabled(...) @disinherit(...) @encoding(...) @history-elt(...) @inherit(...) @params(...) @preserve(...) @prompt(...) @replace(...) @request(...) @sync(...) @validate(...) Example: mdl form@api(post /api/profile)@result(profileResult)@swap(replace)@params(email csrfToken)@disable

When I started learning Data Science, I expected to spend my first week writing Python code, exploring machine learning models, and working with advanced tools. Instead, I spent most of my time in Excel. At first, it felt underwhelming—just rows, columns, and simple spreadsheets. But within a few days, I realized something important: Excel is not a basic tool at all. It is one of the most widely used tools in data analysis, business decision-making, and reporting. 📊 Real-World Uses of Excel Excel is widely used across industries for handling and analyzing data. Some of the most common uses include: Business Analysis - Tracking sales and identifying trend Accounting and Budgeting - Managing Expenses, Profits and Financial reports Marketing Analysis - Measuring campaigns performance and customer behavior Data Entry and Management - organizing large datasets efficiently Businesses rely on Excel because it helps turn raw data into meaningful insights for decision making. 🛠️ Key Excel Features I Learned In my first week, I explored several important Excel Features that help with data organization and analysis: Excel Interface Overview - I first explored how Excel is organized, including Ribbon, Worksheets, Cell, Row, Columns, and formula bar. this helped me understand how to navigate the tool before working with data Data Sorting - Organizing data by numbers, Text and Dates Filtering - Showing only relevant data based on condition Data Validation - Ensuring accurate and consistent data entry Freeze Panes - Keeping header Visible while scrolling through large datasets. These features make working with data much easier, faster and more structured. 🧮 Basic Excel Functions I learned I was also introduced to some basic Excel functions used in Data Analysis. Aggregate Functions - SUM - Add all values in a range - AVERAGE - Calculate the mean of a dataset - COUNT - Counts numerical entries in a dataset Conditional Functions - SUMIF () and SUMIFS()** - Add values that meets one

7 Infra Improvement Strategies to Prevent Next.js Deployment Build Failures in 2026 Recently, our team's deployment pipeline started showing serious instability. Specifically, we encountered recurring build failures related to the chat build. As a result, the entire development team was preoccupied with battling these build failures. Attempts and Pitfalls Initially, I thought the --preload detection logic was the problem. I modified it to detect only specific lines, but this ended up causing issues in other areas. The recurring chat build failures were actually caused by the next.config file not properly recognizing file extensions. I modified it to allow extensions like .mjs , .js , .ts , and .cjs , but even that didn't work correctly at first, leading to some wasted effort. # .github/workflows/deploy.yml (Excerpt from initial version) - name : Run Preload Detection run : | # ... existing logic ... if [[ "$LINE" == *"some_pattern"* ]]; then echo "Preload detected" # ... fi I modified it to detect only specific lines like the above, which led to unintended behavior. // next.config.js (Initial configuration) module . exports = { // ... experimental : { // ... }, // ... }; Regarding extensions, I initially allowed only a few types, and only after experiencing chat build failures did I modify it to support more extensions. Root Causes In the end, it was a combination of several complex issues. There were flaws in the --preload detection logic, and the range of supported extensions in the next.config file was too narrow, which was the direct cause of the chat build failures. Additionally, there was confusion arising from the chat server builds being inconsistent between P1/P2 and P0 stages. Problems also occurred because the .next directory was not preserved, and the smoke gate was too lenient, failing to catch build failures. Finally, there was an unexpected side effect where the next/font/google library caused GCE outbound connection errors. Solutions To address these

1. update WSL environment sudo apt update && sudo apt upgrade -y 2. install dependencies If you don't use -hf option, you don't need to install libssl-dev in this step. sudo apt install build-essential cmake git libssl-dev -y If nvidia-smi shows a GPU/GPUs on your terminal, you will need to install the tooklit. This will take some time. sudo apt install nvidia-cuda-toolkit -y 3. clone the repo Build llama-cli and llama-server. This step also will take some time. If you don't plan to use -hf option, you don't need to use -DLLAMA_OPENSSL=ON . git clone https://github.com/ggerganov/llama.cpp cd llama.cpp cmake -B build -DGGML_CUDA = ON -DLLAMA_OPENSSL = ON cmake --build build --config Release # no GPU git clone https://github.com/ggerganov/llama.cpp cd llama.cpp cmake -B build cmake --build build --config Release 4. run the model Run gemma-4-12b-it with cli and server. unsloth/gemma-4-12b-it-GGUF · Hugging Face We’re on a journey to advance and democratize artificial intelligence through open source and open science. huggingface.co ./build/bin/llama-cli -hf unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL > hello [ Start thinking] The user said "hello" . The user is initiating a conversation. Respond politely and offer assistance. * "Hello! How can I help you today?" * "Hi there! What's on your mind?" * "Hello! Is there anything I can assist you with?" [ End thinking] Hello! How can I help you today? [ Prompt: 19.5 t/s | Generation: 11.8 t/s ] or run web-ui ./build/bin/llama-server -hf unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL --port 8080 optional download model from huggingface mkdir -p models wget -O models/gemma-4-12b-it-UD-Q4_K_XL.gguf https://huggingface.co/unsloth/gemma-4-12b-it-GGUF/resolve/main/gemma-4-12b-it-UD-Q4_K_XL.gguf

If you're building an AI app (image generation, transcription, an agent, anything that calls a model) you've probably realized a flat "$10/month" doesn't work. Every action costs you real money in GPU/API spend, so a single power user can torch your margins. The answer is usage credits : users buy a balance, each action spends some. Credits sound trivial. They are not. I've shipped about 10 small AI/SaaS apps, and the credit layer is where I got burned every single time. It took three patterns to fix it for good. Here they are, with copy-pasteable code for Next.js + Supabase + Stripe. Get these right and your billing won't oversell, double-charge, or strand a user's money. The three things everyone gets wrong Overdrawing. Two requests arrive at once, both read "balance = 1," both spend. Now the balance is negative and you gave away work for free. Double-granting. Stripe retries webhooks (it will ), and if you grant credits on every delivery, a $9 purchase becomes $18 of credits. Forgetting the refund. The AI job fails after you've already charged the credits. The user paid for nothing and emails you angry. Let's kill all three. Part 1. The atomic spend (overdraw becomes impossible) The mistake is doing the check in your app code: // DON'T: read-then-write has a race condition const { balance } = await getBalance ( userId ); if ( balance < cost ) throw new Error ( " insufficient " ); await setBalance ( userId , balance - cost ); // two concurrent requests both pass the check Do it in the database, in one statement, with the guard in the WHERE clause: -- balances: one row per user create table credit_balances ( user_id uuid primary key references auth . users ( id ) on delete cascade , balance integer not null default 0 check ( balance >= 0 ), updated_at timestamptz not null default now () ); -- append-only ledger = audit log + idempotency guard (see Part 2) create table credit_ledger ( id bigint generated always as identity primary key , user_id uuid not null referen

Seller of the Sound Blaster Katana V2X doesn't consider the behavior a vulnerability.

The companies announced the deal on Friday, just one week ahead of SpaceX's historic IPO.

Releasing sterilized flies can crash a local population of flesh-eating screwworms. But the US currently has limited capacity to produce them.

GM wants to slash EV prices by deploying new battery tech up to a year earlier than planned. This building is key to making that happen.

Lectric, which says the U.S. market is ripe for competition and choice, has launched three new brands in the past six months.

This quick tutorial shows how to install ASDF Version Manager on Arch Linux and use it to install PHP 8.5. Install Required Dependencies First, install the required packages and build dependencies: yay -S base-devel libpng postgresql-libs re2c gd oniguruma libzip libsodium You may also want to install additional common dependencies: yay -S curl git openssl zlib libxml2 sqlite Install ASDF Clone the ASDF repository: git clone https://github.com/asdf-vm/asdf.git ~/.asdf --branch v0.18.0 Add ASDF to your shell configuration. Bash echo '. "$HOME/.asdf/asdf.sh"' >> ~/.bashrc echo '. "$HOME/.asdf/completions/asdf.bash"' >> ~/.bashrc source ~/.bashrc ZSH echo '. "$HOME/.asdf/asdf.sh"' >> ~/.zshrc echo '. "$HOME/.asdf/completions/asdf.bash"' >> ~/.zshrc source ~/.zshrc Verify installation: asdf --version Add the PHP Plugin Install the PHP plugin for ASDF: asdf plugin add php https://github.com/asdf-community/asdf-php.git Install PHP 8.5 List available PHP versions: asdf list all php Install PHP 8.5: asdf install php 8.5.0 Set PHP 8.5 as the global default: asdf global php 8.5.0 Reload your shell: exec $SHELL Verify the installation: php -v Expected output: PHP 8.5.x ( cli ) Useful ASDF Commands List installed PHP versions: asdf list php Install another PHP version: asdf install php 8.4.0 Switch globally: asdf global php 8.4.0 Switch locally for a project: asdf local php 8.5.0 Optional: Install Composer After installing PHP, install Composer globally: php -r "copy('https://getcomposer.org/installer', 'composer-setup.php');" php composer-setup.php sudo mv composer.phar /usr/local/bin/composer rm composer-setup.php Verify: composer --version

"The whole conversation shifted from tokenmaxxing and 'go fast' to 'we need guardrails, how do we control this?'"

Microsoft has two livestreams planned for this Sunday.

“Why don’t we just frickin’ call Apple?”

AI companies are using serif to project humanity. Critics are calling it “tasteslop.”

When building a small content site, the framework is usually not the hardest part. The harder part is deciding what each page should be responsible for. A lot of sites start as a simple article list. That works for a while, but it becomes messy when visitors arrive with different search intents. Some users want to learn what something means. Some want download or setup information. Others are trying to fix a specific issue. Those users should not all land on the same generic page. The structure I use For a small Next.js content hub, I like to separate routes by intent: Homepage: broad entry point Learn hub: basic explanations and guides Learn detail pages: specific guide topics Download page: download or install intent Fix hub: troubleshooting entry point Fix detail pages: specific issue pages English and Japanese routes: language-specific entry points This structure is simple, but it keeps the site easier to maintain. Page role comes first Before writing a page, I define its role. A learn page answers what something is, how it works, and what a beginner should understand first. A download page answers where a user should get something, what should be checked before installing, and which platform or device matters. A fix page answers what is not working, what should be checked first, and whether the problem is related to permissions, notifications, device settings, or installation. The page role decides the title, description, internal links, and body structure. Why this helps SEO This approach helps avoid pages competing with each other. For example, a download page should not try to rank for every tutorial query. A troubleshooting page should not read like a general homepage. Each page can link to related pages, but the primary intent stays clear. That makes the site cleaner for both users and search engines. Metadata and sitemap discipline In a Next.js App Router project, I also like to keep metadata and sitemap updates close to the route change. For example: If

Channel Frekuensi (MHz) 1 2412 2 2417 3 2422 4 2427 5 2432 6 2437 7 2442 8 2447 9 2452 10 2457 11 2462 12 2467 13 2472 14 2484 Catatan: Channel yang tidak tumpang tindih adalah channel 1, 6, dan 11. Channel 12 dan 13 legal di Indonesia dan Eropa, tetapi dilarang di Amerika Serikat. Channel 14 hanya digunakan di Jepang, tepatnya pada standar 802.11b .