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Working through a practical reference to moving a change through Git took me through the process of taking a single change from my local computer, committing it in Git, and pushing it to GitHub where others would be able to see it. This was written for complete Git Novices and so I approached it without any prior experience of using Git. By the end, you will be able to move a change through all four Git stages and confirm it's visible on GitHub with a clear commit history. Who This Is For Anyone that want to work with Git in the terminal Users who find git add , git commit , and git push unclear No prior Git experience required Prerequisites Git installed ( git --version to check) A terminal or command-line application A GitHub account (create one if needed) We'll use a small sales data project as a running example. You do not need Python or Pandas installeda as we will be only tracking files, not running code. The Four-Stage Flow Every change travels in one direction: Working Directory → Staging Area → Local Repository → Remote Repository Working directory : where everything is worked on Staging area : choosing what goes in Commit : your historical record for everything worked on Push : share it so collaborators can see it Setup: Initialize a Repository Create a project folder and make it a Git repository: mkdir monthly-sales cd monthly-sales git init Expected output: Initialized empty Git repository in /path/to/monthly-sales/.git/ git init creates a hidden .git folder where Git stores the entire history. From now on, Git watches this folder. Usage 1. Working Directory (Untracked Files) Create a raw data file: echo "date,region,revenue" > sales_data.csv echo "2026-01-01,East,1200" >> sales_data.csv Check its status: git status Expected output: On branch main No commits yet Untracked files: (use "git add <file>..." to include in what will be committed) sales_data.csv Untracked means Git can see the file but isn't following it yet. This is the default for new files.

Introduction People, especially beginners, face challenges when making changes to their projects and pushing those changes to Github. Some of the challenges faced include Git command, authentification, configuration etc. This article aims to explain the process of building a Github project, from creating local directories/folders to uploading the project to Github using Git and secure shell(ssh). I will use a project titled Poor Performance in 2025 National Exams as a case example to illustrate the steps involved in creating a project. The process is outlined in the following steps: STEP 1: Creating directories The first step of a project is to establish a well organized directory structure. This helps keep project files organized logically, making them easier to manage, access and maintain throughout the development process. We create our project directories within Desktop and OneDrive. Desktop is basically a special directory used by windows to store files and shortcuts while OneDrive is a microsoft's storage store. In our case we start by navigating to the desktop directory using Git. For example, cd desktop means 'go to desktop' Next, Use ls to list all the directories and files in desktop. Once you have navigated the desktop directory, Proceed to making your first project directory. We use mkdir commandto create a new directory. For example, mkdir "Poor-performance-in-2025-National Exam" . Next, we create additional directories within our newly created project directory. In this case we will create a directory called Data, which will store the datasets required for the analysis. For example, mkdir data STEP 2: Creating Files Files are essential part of building a project. In this step, we will create a README.md file. A README.md gives an overview of the project and describes its purpose, structure and usage in a clear and understandable way. README.md is written using Markdown language. we use touch to create files. For example, touch README.md STEP 3: Printin

You have probably written HTML forms before, and so the structure below resonates with you. Perhaps you even smile because, this one, you understand. <form> <input type= "text" /> <button> Submit </button> </form> If you have done this, you know what happens when you click the button. The whole form reloads, the changes or inputs are cleared. This is the default behavior of forms in HTML. In React, we handle every step and every stage so that we have control over the data and the behavior of the form and data. The above signature represents what we call UNCONTROLLED INPUT . This means that there isn't a single source of truth to the value of this field, hence it can change to anything, and any value In addition to the above attributes, we will add value and onChange props to the input element as below: <input type= "text" value= {} onChange= {}/ > value represents the content of the input field e.g. the name text that the user enters in a Name field. onChange is the function that will be triggered everytime the input changes. Whenever a key is pressed within this field, this function will be invoked. Controlled inputs have their values set and manipulated by states, as we saw in Part 1 of the series. Uncontrolled inputs on the other hand do not have a manager that will dictate what goes into the field and when. Now let's write our first React Input, we'll keep it simple. import { useState } from " react " function Form (){ const [ name , setName ] = useState ( "" ) return ( < input type = " text " value = { name } onChange = {( event ) => setName ( event . target . value )} / > ) Let's look at what happens in the above. We have declared a state [name,setName] . name is the state variable setName is a function used to update the variable We then initialized an input element with properties value and onChange Note that, when the value of an input is set, that will always be the value even if you type something into the box. That is the essence of controlled input. The

Introduction In this article I will explain my practical experience of creating Git repository,connecting it to GitHub using SSH,commiting my files and pushing the project to a remote repository. Step 1 :Creating My Local Project The first step is to create a folder through file explorer which as the best option for me and name it (my-project) Next step was to open GitBash and run the command cd my- project To open the file directory Step 2 : Initializing Git This is to tell Git that my my-project folder should become a Git repository. To initialize Git I ran the command: git init Step 3:Repository Status Check After initializing Git, I used : git status This command is very useful as it can be used throughout the process it tells what's happening inside my repository. At some point people may encounter: On branch main Nothing to commit, working on a tree clean It could seem like an error,however I learned that this means Git has checked my project and found no changes that need to be committed. Another situation that I encountered is where Git told me that the older my project was already initialized .This taught me to use the command: git status To understand the current status of my project. Step 4:Connecting GitHub Using SSH The next thing I learnt was on how to generate SSH key which will be used to connect my computer securely to GitHub. To generate an SSH key I ran the command: ssh-keygen -t ed25519 -C "your_email@example.com" Under the double quotes use the email used for your GitHub account. Then press entre to save it on the default location. You'll then be told to enter passphrase which is simply a password,create a simple one which you can memorize easily like 1234.Then entre it again when asked. You then start the SSH agent Run; eval "$(ssh-agent -s)" Then add your SSH keyy Run: ssh-add ~/ .ssh/id_ed2559 It will ask you to entre the passphrase you created. Copy your public key y running the command: cat ~ .ssh/id_ed25519 .pub Add the entire line generat

A free model quota is a budget, not a gift. You should treat it like one if you plan to build anything on top of it. I learned this the hard way when my prototype stopped responding in the middle of a demo. I had silently burned through the monthly allowance, and the provider cut me off without warning. This article shows how I built a small token budget alarm on a free server. It watches a free model's usage and warns me before the quota runs out. Most developers track their cloud spend religiously but ignore the token consumption of free models. The free tier feels like a gift, so we assume it will last forever. Then the provider cuts us off at the worst moment, and we scramble to find the cause. A token budget alarm removes that uncertainty by measuring your actual burn rate. It projects the exhaustion date and alerts you before you hit the wall. The design is deliberately small: a reverse proxy sits in front of the model endpoint. It records the token usage from every response and stores it in a local database. A background thread then computes the average consumption over a sliding window. It compares that rate against the remaining allowance and fires a webhook when the projection looks dangerous. You can run this entire stack on a free server, which is exactly what I did with MonkeyCode's free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The proxy itself is a tiny Flask application that forwards requests to the model. It extracts the usage field from each response and records the token count. If your provider does not return a usage object, you can estimate the token count with a simple heuristic. Dividing the character count by four is a rough but workable approximation. The important part is that every request is accounted for, because a single long prompt can consume more than a hundred small ones. from flask import Flask , request , Response import requests import sqlite3 import time app = Flask ( __name__

Every open-source tool has the same brutal first 90 days: zero users, zero signal, no idea whether anything works. I have shipped several free CLI tools and browser tool sets. This is the exact funnel I use — no ads, no paid growth, no "build in public" theater. Just a repeating sequence of small, concrete actions. Step 1: Make the Tool Trivial to Try The first rule: npx must work. If a reader has to install, configure, and read a README before running the first command, the funnel is already broken. npx @wuchunjie/dotguard . That is the entire onboarding. Zero dependencies, no config, instant output. The first 10 seconds decide whether the reader comes back. Step 2: Publish One Article Per Angle Not one article. One per angle , spread over time: Tutorial — "Scan your .env files in 1 command" (the how) Comparison — "Why I stopped using X" (the why) Listicle — "5 tools for Y" (the discovery) Workflow — "My dev setup" (the context) Security/devops — "Your CI is missing this" (the fear) Each article targets a different search intent. A developer looking for "pre-commit secret scan" lands on article 4, not article 1. The funnel is wide because the angles are wide. Step 3: Cross-Link Everything Every article mentions every tool. The footer of a snippet article lists the scaffolder and the scanner. The GitHub repo links to the articles. The npm README links to the articles. The effect is compounding: a reader of article 3 meets four tools, not one. Your content becomes a network instead of a pile. Step 4: Make the GitHub Repo the Hub The repo README is the landing page that never goes stale: One-line description per tool Install/run commands (copy-paste ready) Links to every article A donation link, present but quiet GitHub is where developers actually trust. Stars and forks are the signal that converts "interesting article" into "let me try it". Step 5: Add the Quiet CTA One line at the end of every article: If this saved you time, a Ko-fi keeps the next tool coming. No

I built an OLX scraper for 24 countries — the boring version that actually ships OLX runs classifieds in about two dozen countries. Same brand, different domains, different anti-bot setups. Everyone scraping it does one country at a time. I got tired of forking. So I put 24 countries behind one input. country: "id" or country: "pl" or country: "br" — same schema out. It's live on Apify as primesieve/olx-global-scraper . One file. No browser. Here is the boring part that matters. What it does Input: { "country" : "id" , "keywords" : [ "iphone 13" ], "maxResults" : 50 , "maxPages" : 3 , "proxyConfiguration" : { "useApifyProxy" : true , "apifyProxyGroups" : [ "RESIDENTIAL" ] } } country — two-letter code ( id , pl , in , br , ua , pt , ro , bg , kz , uz , pk , za , ng , ke , eg , lb , ph , co , ar , pe , ec , gt , az , ma ). Default id . keywords — one or more search terms. Each runs sequentially. maxResults / maxPages — caps. Defaults 50 / 3, max 1000 / 30. proxyConfiguration — optional for Indonesia, required for the other 23. Output — same shape every country: { "listingId" : "123456789" , "title" : "iPhone 13 128GB mulus" , "price" : 6500000 , "priceText" : "Rp 6.500.000" , "currency" : "IDR" , "city" : "Jakarta Selatan" , "location" : "Tebet, Jakarta Selatan, DKI Jakarta" , "images" : [ "https://...jpg" ], "thumbnailUrl" : "https://...jpg" , "listingUrl" : "https://www.olx.co.id/item/123456789" , "country" : "id" } Title, price (numeric plus display text), currency, location, images, URL. No seller PII beyond what the listing page shows. No tricks. Try: https://apify.com/primesieve/olx-global-scraper The boring stack // no playwright, no puppeteer // apify + fetch + cheerio. That's it. The scraper is one file. Apify SDK for input, dataset, and pay-per-event. Native fetch for HTTP. cheerio for the HTML path. Undici ProxyAgent when a proxy is configured. Node 20, 512 MB, 600s timeout. I check the endpoint before I write the scraper. Indonesia answered with clean JSO

Cómo solucionar el error “Enable JavaScript and cookies to continue” Este mensaje aparece cuando Cloudflare (u otro proxy de seguridad similar) bloquea la solicitud porque detecta que el cliente no cumple con los requisitos mínimos de seguridad: JavaScript deshabilitado o cookies deshabilitadas/expiradas . 🔍 Causa técnica Cloudflare implementa mecanismos de protección como: JavaScript Challenge : El navegador debe ejecutar un script para demostrar que no es un bot. Cookie de verificación : Tras superar el desafío, Cloudflare emite una cookie ( __cf_bm o cf_clearance ) que valida la sesión. Si el cliente (navegador o cliente HTTP personalizado) no ejecuta JavaScript o no maneja cookies correctamente, la validación falla y se muestra este mensaje. ✅ Solución definitiva (por escenario) 🌐 Si eres un usuario final (navegador) Habilita JavaScript : Chrome: Configuración > Privacidad y seguridad > Sitios web no seguros > Habilitar JavaScript . Firefox: Preferencias > Privacidad y seguridad > Permisos > Habilitar JavaScript . Habilita cookies de terceros (si usas extensiones como uBlock Origin o Privacy Badger): Añade el dominio a la lista blanca. Desactiva temporalmente los bloqueadores para probar. Borra cookies y caché del dominio afectado. Reinicia el navegador y vuelve a cargar la página. 🧪 Si eres desarrollador (automatización / scraping / cliente HTTP) ❌ No uses requests o curl sin soporte JS/cookies → fallarán siempre . ✅ Opción recomendada: Usa un navegador headless con soporte JS y cookies # Ejemplo con Playwright (recomendado) from playwright.sync_api import sync_playwright with sync_playwright () as p : browser = p . chromium . launch ( headless = True ) context = browser . new_context () page = context . new_page () # Navega a la URL (Cloudflare se resolverá automáticamente) page . goto ( " https://ejemplo.com " , wait_until = " networkidle " ) # Si aún falla, fuerza espera tras el desafío try : page . wait_for_selector ( " #challenge-error-text " , timeout = 5

Introduction Hello,I'm currently a data science student and this is my understanding of git workflow, from creating folders on my local computer to adding files, pushing and having them on my github repository. Working directory This is the active folder created in the local machine which will have all the files related to the project. We can create a folder on terminal by following the steps, -Launch your terminal -cd desktop :this is to ensure that we are in the desktop folder -mkdir data :this is to create a new folder on desktop -touch school.py :this is to create a python file inside the data folder -git status :this tells us the repository we are working in Staging This allows us to prepare to save the files that we have created. We can save a specific file or all files at once. For example;assuming we have three different files eg.schools.py ,books.py ,teachers.py -git add . :this saves all the files in the folder -git add schools.py :this saves only the schools files Commit This allows us to save the files from the staging step. .git commit -m "creating schools files" .git commit :saves the files to git hub .-m :this is a message that explains the change that happens in the folder ." " :this briefly explains the change that happened Push This allows us to move our work from our local computer to git hub. git push origin main ;origin points us to our online git hub while main is the name of the branch where we are making the changes

An AI assistant will write you a query in ten seconds, the query will run, and the number that comes back will look completely reasonable. This page gives you the five checks that tell you whether that number is right. They take about two minutes, they need no tools beyond the database you already have, and they catch the four mistakes AI-written SQL actually makes. The order matters. The checks are arranged cheapest first, so the first one costs a single row count and the last one costs a short conversation. Most wrong queries fall to the first two. The short version. A query that runs has only passed a grammar check. The number is right when the rows, the filters and the denominator match the question you asked. The database only takes a query as far as the first gate. Why a query that runs can still be wrong Before the list: what do you think the database actually checks when it accepts a query? Grammar. That is the whole list. Spell a table name wrong and you get an error. Sum the wrong column, join in a way that doubles rows, or filter after grouping when the question needed it before, and you get a clean result set with a wrong number in it. Every mistake on this page is valid SQL. AI assistants add one specific difficulty: their queries are fluent. The aliases are tidy, the formatting is clean, and the shape looks like something a careful person wrote. Fluency reads as correctness, and it is not the same thing. Treat an AI query the way you would treat a first draft from a new colleague: with respect, and with the row counts open. The table the examples run on Everything below runs on one small shop dataset, so every number can be checked by hand. Thirteen orders in July, five customers, and a refunds table where two orders were refunded in two parts. Eleven of the thirteen orders are completed; one is refunded, one is pending. There is also a staff_accounts table listing internal accounts, and it contains one NULL row, because real lookup tables usually do.

By Michael Nocito , data analyst · Published August 8, 2026 By the end of this page you will be running real SQL against a real database with nothing installed, and you will know which of the free browser tools suits which job. You will also know the thing none of them puts on the front page: some of them run entirely inside your browser, and some upload whatever you paste to a stranger's server. That difference decides what you are allowed to practise on. Here is what to actually do today. If you want a database already loaded and questions already written, open sql-practice.com . If you want to create your own tables and share the result with someone, open DB Fiddle . Both start working immediately with no account. The short version: browser-only tools keep your data on your machine, server-backed tools do not, and neither kind is the right place for anything from work. Where the data goes is the one idea that should drive your choice, so it gets the picture. The original carries a diagram here. In words: Two panels side by side, each drawn as a laptop outline containing a browser window. In the left panel a small data box sits inside the browser window, with a short circular arrow looping back into itself, showing the data never leaves the laptop. In the right panel the same data box has a long arrow leading out of the laptop, across a gap, and into a separate server rack drawn beyond the laptop's edge, with a copy of the data box now sitting in the rack as well. The original box remains, showing the data has been copied out rather than moved. Every tool below was opened and checked on 8 August 2026. These sites change often, so the descriptions describe what was actually on screen, and anything I could not confirm by looking is not claimed here. 1. Run your first query, right now Before the explanation: what do you think has to exist on your computer for a SELECT statement to return rows? The honest answer is nothing at all, and that surprises people who have sp

By Michael Nocito , data analyst · Published August 8, 2026 By the end of this page you will have a real database sitting on your own computer, with 11 tables, 3,503 tracks and 412 customer invoices in it, and you will have run a query that proves every table arrived intact. Then you will run a join across two of those tables, which is the thing a single spreadsheet can never teach you. It takes about five minutes and costs nothing. Here is what to actually do today. Download the Chinook database file, open it in DB Browser for SQLite, and run one query that counts the rows in every table. If the counts match the ones printed below, you have a working practice environment and you can stop shopping for one. The short version: get Chinook_Sqlite.sqlite , open it, count the rows, then join two tables. Northwind and Sakila are the other two names you will see, and there is a table further down saying when each is the right pick. The reason a sample database beats the CSV you already have is one idea, so it gets the picture. The original carries a diagram here. In words: Two panels side by side. The left panel holds a single grid of rows and columns, standing alone with nothing attached to it. The right panel holds four smaller grids arranged around each other. A highlighted column at the edge of each small grid is joined by a solid line to a matching highlighted column on a neighbouring grid, so all four grids are wired together into a connected shape. The left panel has no lines at all, because there is nothing for a line to reach. Every number on this page is real. I downloaded Chinook v1.4.5 and Northwind on 8 August 2026 and ran each query with SQLite 3.51.1. The counts, the outputs and the row multiplication are what came back, not what should have come back. If you have no database software at all yet, how to set up a SQL database is the fifteen-minute version of that step, and this page picks up right after it. 1. Why one CSV is not enough Before the explanation:

Git is a version control system. Version control, also known as source control, is the practice of tracking and managing changes to software code. Version control systems are software tools that help software teams manage changes to source code over time. Git is used for: Tracking code changes Tracking who made changes Coding collaboration Setting up a new Repository A Git repository is a folder that Git tracks for changes. The repository stores all your project's history and versions. Add files to the folder. The following describes how to set up a new repository: Git Init Initializes git user@localhost $ git init This creates a hidden folder called .git inside your project. This is where Git stores all the information it needs to track your files and history. To see which files are in your project folder, use the ls command: user@localhost $ ls To Check if Git is tracking your new files: user@localhost $ git status The files here could either be tracked or untracked:- Untracked Files Files you've created or copied into the folder, but haven't told Git to watch. Tracked Files Files that Git is watching for changes. To make a file tracked, you need to add it to the staging area. Git Staging Tells Git exactly which files you want to include in your next commit. user@localhost $ git add . Common Commands git add . Stages all new, modified, and deleted files in the current directory and its subdirectories. git add <file> Stages a specific file. git add -A (or --all) Stages all changes across the entire repository, regardless of your current folder location. git add -u Stages modifications and deletions of already-tracked files, ignoring completely new (untracked) files. git add *.txt Stages all files matching a specific pattern (e.g., all text files). Git Commit A commit is like a save point in your project. It records a snapshot of your files at a certain time, with a message describing what changed. user@localhost $ git commit -m " Describe your changes" Pushing Chan

One broken window invites another TL;DR: Don't reuse an existing getter to bolt on new business logic from outside the object. Problems 😔 Duplicated business rules Broken encapsulation Scattered comparison logic Hidden domain knowledge Fragile refactoring Law of Demeter violation Solutions 😃 Add real behavior methods Keep comparisons inside object Pass collaborators, not primitives Reserve getters for rendering Follow tell, don't ask Refactorings ⚙️ Refactoring 027 - Remove Getters Maxi Contieri Maxi Contieri Maxi Contieri Follow Apr 18 '25 Refactoring 027 - Remove Getters # webdev # programming # beginners # java 3 reactions Add Comment 17 min read Refactoring 013 - Remove Repeated Code Maxi Contieri Maxi Contieri Maxi Contieri Follow Jun 16 '24 Refactoring 013 - Remove Repeated Code # webdev # beginners # programming # tutorial 2 reactions Add Comment 3 min read Context 💬 An object exposes a getter for one legitimate reason: some other part of the system needs to read that value, usually to display it. Getters are a code smell, but this one gets a pass, for now. Later on, you discover that you need new business logic that depends on the same value. You already have the getter, so you write a function outside the object that calls it and does the comparison itself, breaking the encapsulation principle. Someone else needs slightly different logic based on the same value. They also call the getter and write their own version of the comparison. Now two places decide what that value means , and neither of them is the object that owns it. Typical. You didn't add a second getter this time. You reused the first one, because it was already there. That's the trap. The getter existed for one reason, and you let it justify skipping the real fix: a method on the object that answers the question itself, instead of handing out the raw value for every caller to interpret on their own. Sample Code 💻 Wrong 🚫 // Food needs to show its use-by date on the shelf // label, so useByDate(

Building fully autonomous "AI agent teams" with automated code merging often introduces subtle architectural defects, circular refactoring loops, and codebase degradation. Two agents running in parallel do not constitute independent ground truth: if the author agent makes a logical error, a reviewer agent operating on similar prompt foundations may easily overlook it. A reliable multi-agent workflow is built not on the illusion of full autonomy, but on strict role separation: a dedicated implementer (Author), an independent verifier (Reviewer), and a human developer who makes the final merge decision (Decision Maker). 1. Role Boundaries: Author, Reviewer, and Human Engineer In an effective AI development workflow, each participant has a closed, well-defined scope of responsibility: Role Core Responsibility Input Artifacts Output Artifacts Author Agent Code implementation, local unit tests Task description, completion criteria Git branch, diff, focused test suite Reviewer Agent Edge case discovery, regression checking Git diff, handoff card, verification commands Structured review checklist (Pass/Block) Human Decision Maker Architectural validation, final merge Reviewer summary, CI/CD status Manual merge to main branch 2. Context and Workspace Isolation Never run the author and reviewer agents within the same working directory or shared conversation thread. Isolate them across three operational levels: Session Context : Independent conversation threads prevent mutual hallucination and circular confirmation. Filesystem Isolation : Separate Git worktrees ensure the reviewer inspects only committed diffs without dirty working tree state. Environment Security : API keys and runtime credentials remain in environment variables and are never passed in prompt text. Worktree Preparation Commands: git worktree add ../agent-author -b feat/payment-retry git worktree add ../agent-reviewer feat/payment-retry [!IMPORTANT] API Configuration : Each Claude Code session operates using

A blank application window is not a diagnosis. It is only a symptom that tells you the process reached a different stage than an installer that never launched. The most useful first step is to stop applying fixes and record what Windows is actually doing. 1. Define the failure boundary Treat these as separate cases: No window and no lasting process: investigate the installer, security blocking, architecture, and missing runtime dependencies. A window frame appears but the content area is blank: investigate the rendering layer and online content initialization. The entire window stops responding: capture hang evidence instead of reinstalling a rendering runtime. The UI appears but sign-in or online panels spin forever: check network and account services separately. This boundary prevents a common mistake: diagnosing every white or empty interface as a WebView2 failure. 2. Record the process tree Close the application completely, including any tray process, then start it once. Open Task Manager and note the start time of the main process. Check whether child processes such as msedgewebview2.exe appear at the same time. The presence of a WebView2 process is evidence that the runtime participates in the session. Its absence does not automatically prove that WebView2 is broken; the application may have failed before reaching that stage or may use another rendering stack. 3. Check the installed WebView2 version without downloading anything On Windows, the Evergreen Runtime version can often be read from registry locations under EdgeUpdate. The exact location can vary by installation scope. Use read-only inspection first, and confirm that a non-empty version value is present. Do not install several copies from random download pages. If repair is eventually necessary, use Microsoft's distribution channel and document the version before and after the change. 4. Correlate the failure with Windows logs Reproduce the blank window once, then open Event Viewer and inspect entries

The demo gives a quick look at the workflow for creating short-form videos with trimming, stitching, text overlays, voice tools, themes, and transitions. Built with FastAPI, Next.js, Redis/ARQ, and FFmpeg. Repo: https://github.com/ronin1770/reel-quick OpenSource #Python #FastAPI #NextJS #FFmpeg #VideoAutomation #DeveloperTools #AI

Affiliate disclosure: I’m an independent Payhip Partner. The optional signup link at the end is my partner link; I may receive a commission from Payhip if a referred seller generates eligible revenue. I am not a Payhip employee or official representative. A creator selling one template or downloadable guide does not need to write a payment backend. The safer architecture is usually: Carrd or static page ↓ Payhip product page or direct checkout ↓ Hosted payment and product delivery Your public page explains the offer. The hosted commerce platform owns the payment flow. No card data, secret keys, or payment logic belongs in Carrd. This tutorial shows two link-based integrations and one optional embed route. Before you start You need: A published product in Payhip Its public product URL A button on your Carrd or static page A clear product description, support contact, and terms In Payhip, the product URL is available from the product’s Share / Embed controls. A typical product URL has this shape: https://payhip.com/b/PRODUCT_KEY Use your real product key in every example below. Option 1: Send visitors to the product page This is the safest default when the buyer still needs details before purchasing. In Carrd: Select the call-to-action button. Set its URL to your full Payhip product URL. Use a descriptive label such as View template details or See what’s included . Preview the page on desktop and mobile. On a conventional static site, the equivalent HTML is just an anchor: <a class= "product-button" href= "https://payhip.com/b/PRODUCT_KEY" > View product details </a> No JavaScript is required. Use this route when the Payhip product page contains important previews, license terms, compatibility notes, or variations that do not fit on your landing page. Option 2: Link directly to checkout If your landing page already gives the buyer everything needed to decide, a direct checkout removes an intermediate page. Payhip documents this URL format: https://payhip.com/buy?link=

Most developers building frontend applications spend a lot of time writing code that communicates with their backend due to the traditional approach (using APIs). This is not because the logic is hard to implement, but because the communication itself is complex. When using standard APIs, we build routes, define request and response models, generate clients, and keep multiple layers on track with application updates. Instead of exposing backend functionality through REST endpoints and consuming it through HTTP clients, Graftcode exposes backend methods directly and generates packages that applications can install and use as dependencies. The result is a communication model that is like you are calling a library rather than consuming an API with strongly typed clients. Working with Graftcode is very simple: install your library and call its functions. In this article, we'll be building a simple task tracker or to-do list application using React and a TypeScript backend to see what working with Graftcode looks like. In this blog post, we will learn the following: Why API layers require you to maintain APIs manually How Graftcode exposes backend functionality through Graftcode Gateway How Graftcode Vision helps discover backend capabilities Familiarity with APIs and fetch() requests How React applications can use TypeScript backend logic without building API routes Why strongly-typed backend packages can improve developer experience Prerequisites Let’s get our hands a bit dirty, but before we do, there are some need-to-haves to get you started. Let’s have a look at that in this section: Latest Node version installed on your machine Basic knowledge of React and TypeScript Familiarity with how APIs and fetch requests work (for understanding how easy Graftcode’s approach is) A Graftcode account Graftcode gateway installed on your local machine With these prerequisites, you’ll first understand why most to-do list applications rely heavily on APIs for their logic and what c

Here's a modal close-on-Escape that quietly does the wrong thing: function Modal ({ onClose }: { onClose : () => void }) { useEffect (() => { const onKey = ( e : KeyboardEvent ) => { if ( e . key === " Escape " ) onClose (); }; window . addEventListener ( " keydown " , onKey ); return () => window . removeEventListener ( " keydown " , onKey ); }, [ onClose ]); return < div role = "dialog" > … </ div >; } If the parent passes an inline onClose={() => setOpen(false)} — and it almost always does — onClose is a new function on every render, so this effect tears the listener down and adds a fresh one on every single render of the parent. Drop onClose from the deps to stop the churn and you get the other bug: the listener now holds the first render's onClose forever, and closing the modal calls a stale closure. You can't win this with a dependency array, because the two things you want are in direct conflict: subscribe once , but always run the newest handler . The fix is to separate them — register the listener on a stable identity, and call through a ref that's kept current. useEventListener from @reactuses/core is that split, packaged. This post covers what it actually does under the hood, the four ways to name a target, exactly what TypeScript infers for each one (this part surprises people), the options that don't retrigger, and the two gotchas worth knowing before you ship it. Quick Start npm install @reactuses/core import { useEventListener } from " @reactuses/core " ; function Modal ({ onClose }: { onClose : () => void }) { useEventListener ( " keydown " , ( e ) => { if ( e . key === " Escape " ) onClose (); }); return < div role = "dialog" > … </ div >; } That's the whole fix. No dependency array, no useCallback on the parent, no cleanup to remember. The listener is added to window once when the component mounts and removed when it unmounts; the arrow function you passed is re-created on every render and it doesn't matter, because the listener never re-registers