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Bluesky starter packs look like a single thing in the app — a shareable page that lets a new user follow a curated group in one tap. At the protocol level they are three separate records glued together by references, and if you create them from code (we do, as part of an automated outreach pipeline), the decomposition matters: it decides what you can update later, what you can only create once, and where a naive script will quietly make a mess. The three records Everything below is plain com.atproto.repo.createRecord / putRecord calls against your own PDS — no special API surface. 1. The list — app.bsky.graph.list . A starter pack is backed by an ordinary Bluesky list with purpose: app.bsky.graph.defs#referencelist . The list record itself holds metadata — name, purpose, createdAt, plus optional description and avatar. Members live elsewhere. 2. The memberships — app.bsky.graph.listitem . One record per member, each holding the member's DID and the list's AT-URI. There is no "add 20 members" batch call in the record layer: twenty members means twenty listitem creates. Plan for partial failure in the middle of that loop — more below. 3. The pack — app.bsky.graph.starterpack . The record that makes the share page exist. Per the lexicon, name , list , and createdAt are required; list is the AT-URI of the referencelist from step 1, the name is capped at 50 graphemes, and optional feeds can attach custom feeds. The official limits: up to 150 people, up to 3 feeds. The share URL is derivable, not returned: https://bsky.app/starter-pack/{your-handle}/{rkey} where {rkey} is the tail of the starterpack record's AT-URI. The trap: creation is not idempotent Every createRecord mints a fresh rkey. Run your create-starter-pack script twice and you have two packs with two URLs, both live, both indexed — and the one you already shared is not the one your script now reports. There is no natural key (like a title) that the protocol dedupes on. Our rules after learning this: Creation

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__

I work in a 200-person Class II medical device company with two QA/RA folks, three embedded-hardware teams, a small supplier-quality group, and engineers who want automation — not more paperwork. We needed an eQMS that unifies design control (DHF), CAPA, supplier records, and traceability into something engineers will actually use. I evaluated several vendors and want to share a short, practical comparison for teams with that shape. A quick regulatory framing before the list Standards that mattered to us: ISO 13485 and 21 CFR 820, plus MDR-adjacent traceability requirements for components when applicable. A note on risk and regulation posture: don’t treat Article 50 (AI oversight posture for medtech tools) like an apocalypse — it sits in the general enforcement stack. Plan for reviewability and traceability for any AI-assisted workflows, but don’t let fear of hypothetical fines block good tooling decisions today. What I judged vendors on Fit for medtech design control / DHF workflows Traceability across requirements, risk, design, and CAPA Integrations — especially with CRM/supplier systems and engineering tools Operational fit for a 200-person org (configurability, onboarding burden) Capacity for process automation (automated CAPAs, connected workflow) The list — what each vendor practically means for a team like mine 1) Greenlight Guru Why I put it first: Greenlight Guru explicitly lists medical-device as its industry. For a Class II shop that needs DHF, design-control-native workflows, and templates tuned to medtech terminology, that industry focus matters. Fit: Strong for device-focused workflows and teams that want a medtech-first product experience rather than a generic QMS. When to pick: You want a purpose-built medtech experience, quicker ramp for QA/engineering, and vendor docs/templates that speak design control and DHF. 2) Qualio Public positioning: Qualio serves general industries including medical-device and pharma. Fit: Good option if you want flexibil

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:

The US Department of Justice announced on Friday that TikTok will pay $400 million to settle a lawsuit filed in 2024 over allegedly violating the Children's Online Privacy Protection Act (COPPA). In the lawsuit, the DOJ alleged that TikTok collected data from children without notifying parents or obtaining consent and did not delete the accounts […]

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

A newly resolved proposal would allow us to select classes that are a prefix for variations with a wildcard, like .prefix-* . Resolved: CSS Class Prefix Selector originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

Over the past few days, a number of prominent filmmaking content creators including Matti Haapoja and Sam "Kold" Kolder have posted videos of themselves demonstrating what's possible with AI platform Higgsfield. The videos highlight Higgsfield's recently added Seedance 2.5 functionality and pitch these technologies as the future of video production. In response to these videos, […]

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(

Patreon has announced a number of new and overhauled features that are designed to "build a better network - and a better internet," according to CEO Jack Conte. The Patreon roadmap includes discovery algorithm updates, platform and security improvements, and new features for both creators and fans. In a lengthy blog post, Conte outlines each […]