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We're back with another installment of the DEV Weekend Challenge ! If you missed the earlier editions, these are short-form, high-energy challenges designed to fit right into your weekend. We're giving you the heads-up now so you can clear your schedule! How It Works Our challenge prompt will be revealed at launch. Follow #weekendchallenge for updates. You can also keep an eye on the DEV Weekend Challenge page or look out for the official announcement post from the DEV Team . From there, you'll have the entire weekend to build, document, and submit your project. That's all there is to it! Because our community spans every timezone on the planet, we've set the window so that everyone around the world gets at least a full weekend to participate. Important Dates Launch Time: July 10 at 2:00 AM UTC Submissions Due: July 13 at 6:59 AM UTC Here's what that looks like across a few timezones: Timezone Launch Time (Local) Submissions Due (Local) PDT Thursday, Jul 9 at 7:00 PM Sunday, Jul 12 at 11:59 PM EDT Thursday, Jul 9 at 10:00 PM Monday, Jul 13 at 2:59 AM GMT Friday, Jul 10 at 2:00 AM Monday, Jul 13 at 6:59 AM CEST Friday, Jul 10 at 4:00 AM Monday, Jul 13 at 8:59 AM IST Friday, Jul 10 at 7:30 AM Monday, Jul 13 at 12:29 PM JST Friday, Jul 10 at 11:00 AM Monday, Jul 13 at 3:59 PM AEST Friday, Jul 10 at 12:00 PM Monday, Jul 13 at 4:59 PM While the window technically spans more than 48 hours, our goal is to ensure everyone has a full, uninterrupted weekend to work on their project regardless of where they live. What else is happening? Mark your calendars for the upcoming Summer Bug Smash . Bug Smash - Register Now We can't wait to see what you build!

Part 1: Solving GOP Structure and Compatibility Issues Operating a digital television network isn't just about keeping channels on air—it's about maintaining quality that viewers expect and troubleshooting issues before they escalate. When something goes wrong in live broadcasting, every second counts. But how do you quickly pinpoint whether the problem lies in encoder settings, transport stream structure, or temporal metadata? This is where specialized stream analysis tools become essential. In this series of articles, we'll walk through real-world scenarios that broadcast engineers face daily and show practical approaches to diagnosing and resolving them. When File Analysis Becomes Critical While live monitoring catches issues as they happen, file-based analysis is your diagnostic microscope. Here's the typical workflow: something breaks in production, engineers capture a few minutes of the problematic stream, and now they need to understand exactly what went wrong. File analyzers serve three primary purposes: Troubleshooting: Identifying the root cause of broadcast issues Encoder optimization: Fine-tuning compression settings Quality control: Validating compliance with standards and specifications Let's explore how this works in practice with actual tools and techniques. The GOP Structure Problem Here's a scenario every broadcast engineer has encountered: legacy set-top boxes or older TV models suddenly can't play your stream. The audio works, video starts and stops, or you see freezing. The culprit? Often, it's the GOP (Group of Pictures) structure. H.264 has been around since 2003—over 20 years. Almost everything supports it, yet you'll still find legacy equipment that struggles with certain configurations. Specifically, the number of B-frames can make or break compatibility. Why B-frames matter: They enable lower bitrates while maintaining quality by increasing encoding complexity through bidirectional prediction. But this comes at a cost—a more complex refere

Modern software development thrives on rapid iteration. Organizations deploy new features, bug fixes, and infrastructure updates multiple times each day to remain competitive and respond quickly to customer needs. Continuous Integration and Continuous Delivery (CI/CD) have transformed software delivery by automating repetitive tasks and accelerating release cycles. However, speed without security creates significant risk. A fast deployment pipeline that introduces vulnerable code into production can expose organizations to data breaches, service disruptions, and compliance violations. Conversely, excessive manual security reviews can slow innovation and delay valuable releases. The solution lies in integrating security directly into the CI/CD pipeline rather than treating it as a separate checkpoint. This philosophy, commonly known as DevSecOps, enables organizations to deliver software rapidly while maintaining a strong security posture. Understanding CI/CD Pipelines What Is Continuous Integration? Continuous Integration (CI) is the practice of frequently merging code changes into a shared repository. Every commit automatically triggers builds and tests, allowing development teams to identify integration issues early instead of waiting until the end of a project. Frequent integration encourages collaboration, reduces merge conflicts, and improves overall software quality. What Is Continuous Delivery? Continuous Delivery extends Continuous Integration by ensuring that validated code is always in a deployable state. Automated testing, packaging, and release preparation make it possible to deploy new versions with minimal manual effort whenever the business is ready. What Is Continuous Deployment? Continuous Deployment goes one step further by automatically releasing approved changes to production once they pass all quality and security checks. This approach significantly shortens release cycles while requiring a high level of confidence in pipeline automation. Benefi

TL;DR — Angular 22 promoted Signal Forms from experimental to stable. This is not "Reactive Forms are dead." It's a real architectural trade-off, and this post walks through both APIs in full, with production-realistic code, so you can decide feature-by-feature instead of framework-war-by-framework-war. Table of Contents Why This Matters Now The Core Question Reactive Forms: Why It Became the Standard Full Example: Reactive Forms Login Where Reactive Forms Still Excel Signal Forms: What Actually Changed in Angular 22 Full Example: Signal Forms Login Where Signal Forms Shine Side-by-Side: Core Concepts Mapped Deep Dive: Validation Synchronous Validation Cross-Field Validation Conditional Validation with when() Async Validation Deep Dive: Dynamic and Nested Forms Nested Form Groups Dynamic Collections (FormArray-style) Deep Dive: Form State — Dirty, Touched, Errors, Submission Developer Experience and Testing Performance Considerations Interop: Migrating Without a Big-Bang Rewrite Migration Strategy for Enterprise Teams When NOT to Migrate Decision Framework FAQ Closing Thoughts Why This Matters Now With Angular 22 (released June 3, 2026), Signal Forms left experimental status and became part of the stable, supported API — alongside resource() and httpResource() . That's a meaningful milestone: it means the Angular team ran extensive internal case studies across real form-heavy applications at Google before committing to stability, and the interop story with Reactive Forms has matured enough that a big-bang rewrite is no longer the only migration path. At the same time, Angular 22 also flips two important defaults: components now use OnPush change detection by default, and zoneless change detection continues its push toward becoming the standard. Signal Forms is part of that same story — Angular's reactivity model finally speaking one dialect end-to-end, from component state to form state to async data. None of this makes Reactive Forms obsolete. It changes what "the

Redux setup is a ceremony. You create a store, compose your reducers into a root tree, wrap your app in a Provider, register middleware, and configure enhancers — all before you write a single line of feature logic. SDuX Vault™ replaces that entire ceremony with two function calls and zero root configuration. Redux Store Ceremony A typical Redux application requires several files and configuration steps before state management is operational. Here is what a minimal Redux setup looks like for a single feature: // store.ts import { createStore , combineReducers , applyMiddleware } from ' redux ' ; import thunk from ' redux-thunk ' ; import { userReducer } from ' ./reducers/userReducer ' ; const rootReducer = combineReducers ({ users : userReducer , }); export const store = createStore ( rootReducer , applyMiddleware ( thunk ) ); // App.tsx — Provider wrapper required import { Provider } from ' react-redux ' ; import { store } from ' ./store ' ; function App () { return ( < Provider store = { store } > < UserList /> < /Provider > ); } That is 20+ lines of configuration across multiple files — and it only covers one feature. Add a second feature and you are back in the combineReducers file, composing another slice into the tree. Add middleware and you are threading enhancers through applyMiddleware . Add DevTools and you are composing composeWithDevTools on top. Every new feature touches the root configuration. Redux Requirement What It Does createStore() Creates the single global store instance combineReducers() Composes feature reducers into a root tree applyMiddleware() Registers middleware (thunk, saga, etc.) Provider Makes the store available to all components via context composeWithDevTools() Enables Redux DevTools integration ⚠️ Warning: Every entry in that table is root-level configuration. Adding a new feature means editing the root reducer composition, possibly the middleware stack, and potentially the Provider hierarchy. Root configuration is a shared depende

I came into this as a graphic designer, not a software engineer. I didn't have a computer science background, and a lot of what BrandStack needed — authentication, databases, payments, deployment — was new territory for me when I started. What made it possible wasn't some shortcut. It was breaking the problem down into pieces I could actually learn: how user accounts work, how a database should be structured so one person's data never leaks into another's, how to move from test payments to real ones without breaking checkout for actual customers. I made real mistakes along the way. Early on, every user shared the same underlying brand data because I hadn't scoped the database correctly to each account — a serious bug that I only caught by testing with two separate accounts myself. Finding and fixing that taught me more about proper application architecture than any tutorial could have. I don't think being a designer first is a disadvantage for building product. If anything, it means the interface and the experience get real attention, not just the backend logic. But it does mean being honest about what you don't know yet, and being willing to slow down and actually understand a problem instead of copying a fix you don't understand. BrandStack is still a work in progress. But it's a real, working product — built by someone who had to learn most of this from scratch, in public, one bug at a time.

How to Monitor Website Changes Automatically I run a few websites and need to know immediately when something breaks. A CSS regression, a broken layout, a missing section. Manual checking doesn't scale, and text-based monitoring misses visual issues. The {{screenshot-diff}} on Apify takes two screenshots and produces a pixel-level comparison with an overlay showing exactly what changed. How It Works Take a baseline screenshot of the correct state. Then take a current screenshot of the live page. The actor compares pixel by pixel and returns a diff image with changed pixels highlighted, plus a percentage telling you how much changed. import requests , time API_TOKEN = " YOUR_APIFY_TOKEN " def capture_screenshot ( url ): resp = requests . post ( " https://api.apify.com/v2/acts/weeknds~website-screenshot-api/runs " , headers = { " Authorization " : f " Bearer { API_TOKEN } " }, json = { " url " : url , " fullPage " : True } ) run_id = resp . json ()[ " data " ][ " id " ] time . sleep ( 15 ) items = requests . get ( f " https://api.apify.com/v2/acts/weeknds~website-screenshot-api/runs/ { run_id } /dataset/items " , headers = { " Authorization " : f " Bearer { API_TOKEN } " } ). json () return items [ 0 ][ " screenshotUrl " ] def compare_screenshots ( baseline_url , current_url ): resp = requests . post ( " https://api.apify.com/v2/acts/weeknds~screenshot-comparison-tool/runs " , headers = { " Authorization " : f " Bearer { API_TOKEN } " }, json = { " baselineImageUrl " : baseline_url , " currentImageUrl " : current_url , " threshold " : 0.01 } ) run_id = resp . json ()[ " data " ][ " id " ] time . sleep ( 10 ) items = requests . get ( f " https://api.apify.com/v2/acts/weeknds~screenshot-comparison-tool/runs/ { run_id } /dataset/items " , headers = { " Authorization " : f " Bearer { API_TOKEN } " } ). json () return items [ 0 ] baseline = capture_screenshot ( " https://mysite.com " ) current = capture_screenshot ( " https://mysite.com " ) result = compare_screenshots ( b

How I Built 7 Apify Actors and Started Earning Passive Income from Web Scraping A few weeks ago I had zero Apify actors. Now I have seven, all published on the Apify Store, monetized with pay-per-event pricing, and slowly building a passive income stream. Here's exactly how I did it — the strategy, the tech stack, the mistakes, and what I'd do differently. The Strategy: Zero Competition Most new Apify developers go after hot categories. LinkedIn scrapers, Amazon product extractors, Twitter data. Makes sense — those have demand. But they also have dozens of established actors with hundreds of reviews. I took the opposite approach. Find niches with zero existing actors. This means lower total addressable market, but 100% of whatever traffic exists goes to you. No competing on price, no fighting for reviews, no SEO war against actors with years of history. How I found the niches: Browsed Apify Store categories sorted by actor count Searched for common developer pain points with no existing Apify solution Checked search volume for "[keyword] API" and "[keyword] scraper" Verified zero results on Apify Store for each candidate The winners: domain intelligence, screenshot comparison, Swedish company registry, IP geolocation, QR code generation, and link metadata extraction. The Tech Stack Every actor uses the same foundation. Apify Python SDK v3.4 handles input/output, storage, proxy, and deployment. Playwright for JavaScript-heavy sites and screenshots. aiohttp for lightweight API scraping (way faster than a full browser). Pillow for image processing. Deployment is one command: apify push The Actors {{domain-intel}} WHOIS, DNS, SSL, and tech stack in one API call. Uses socket + ssl + python-whois for data collection, no external API dependency. $0.005 per run. {{screenshot-api}} Full-page screenshots via Playwright. Handles lazy-loading, infinite scroll, and viewport sizing. $0.003 per run. {{metadata-extractor}} Open Graph, Twitter Cards, JSON-LD, and meta tags from any

Amazon Web Services (AWS) has announced a major expansion of its AWS DevOps Agent, introducing new release management capabilities designed to assess code changes and autonomously test software before it reaches production. By Craig Risi

Like most developers, I have a handful of small utilities I reach for every day — formatting JSON, decoding a JWT, generating a UUID, testing a regex. For years I just googled "json formatter" and pasted my data into whatever site came up first. Then one day I caught myself pasting a production JWT into a random online parser that POSTs everything to its server. That felt bad. So I built my own toolbox that never sends data anywhere. It's called WeTool — free, no login, and every tool runs 100% in your browser . You can open DevTools → Network and confirm there are zero requests while you use it. Here are the 15 I use most: Everyday JSON formatter / validator URL encode / decode Base64 encode / decode Timestamp ↔ date converter Security & encoding Hash calculator (MD5 / SHA) JWT parser UUID generator QR code generator Text & format Regex tester Text diff Markdown preview SQL formatter Debugging Cron expression parser Color converter User-agent parser Two things that matter to me and might to you: Nothing is uploaded. No backend, no login, no tracking of what you type. Local-only. 15 languages. Most tool boxes are English-only; this one isn't. It's free and I'm actively adding tools — if something you use daily is missing, tell me in the comments and I'll add it. 👉 wetool.site

We build an internal helpdesk, and I want to talk through a problem we only partly solved — because I suspect a lot of you have hit it too, and I'd genuinely like to hear how you handled it. The most requested thing from our users was never "better ticket forms." It was "please make the duplicates stop." Here's the shape of it. A deploy goes slightly wrong at a 40-person company. Within ten minutes you have: a handful of chat messages : "login is broken", "can't get into dashboard???", "deploy looks weird" several error-tracker events (whatever you run — Sentry, Rollbar, an APM): TokenExpiredError ×2, a 401 spike on /api/auth , a 5xx spike on auth-svc a couple of emails to IT : "access token expired", "need login reset" Nine items across three channels. One root cause: token rotation broke in that deploy. Whoever's on rotation spends the morning proving that, instead of fixing anything. We wanted to automate the recognition step — "these are the same thing" — not the fixing step. This is the honest version: what we tried, the small thing we actually shipped, and the parts we haven't cracked. If you've built something similar, I'd love to be told what we got wrong. Attempt 1: rules and keywords (broke immediately) The obvious first cut: normalize ticket text, match on keywords and categories, merge on high overlap. It fails on the example above, and it fails structurally: "login is broken" and TokenExpiredError share zero tokens. The human on rotation isn't string-matching — they know a deploy just happened, they know what auth-svc does, they've seen this failure shape before. Rules encode none of that. Rule systems also rot. Every incident teaches you a new synonym for "it's down," and six months in you own a regex museum nobody wants to touch. Maybe you've kept one of these healthy long-term — if so I'd honestly like to know how. Attempt 2: embed everything, cluster by similarity (the one we didn't ship) The tempting next move: embed ticket text, cluster on cosine

The first question I ask when reviewing a frontend architecture is: where does the state live relative to where it is used? In most codebases I have reviewed, the answer is "in a global store, regardless of scope." This is the wrong default. The rule State should live as close to its consumers as possible. If only one component needs it, it is component state. If a subtree needs it, it is a context or service scoped to that subtree. Global state is for truly global concerns: authentication, locale, theme.

Most async code in frontend apps has a hidden bug: it doesn't stop when it should. A user navigates away mid-request. A component unmounts. A newer search query supersedes the previous one. The old network call keeps running, eventually resolves, and tries to update state that no longer exists. In React, that's the infamous warning: "Can't perform a React state update on an unmounted component." In vanilla JS, it silently delivers stale data. AbortController is the browser's built-in solution. It's been in every major browser since 2018 — old enough that there's no excuse not to use it. But most tutorials skip it, most codebases use it inconsistently, and most devs reach for it only after they've debugged a flicker one too many times. Here's the pattern, end to end. The race condition you already have function SearchResults ({ query }: { query : string }) { const [ results , setResults ] = useState < Result [] > ([]); useEffect (() => { fetch ( `/api/search?q= ${ query } ` ) . then ( r => r . json ()) . then ( data => setResults ( data )); // runs even if query changed }, [ query ]); return < ul > { results . map ( r => < li key = { r . id } > { r . name } </ li >) } </ ul >; } When the user types "re" and then "rea" before the first request finishes, two fetches are in flight simultaneously. The request for "re" might complete after the request for "rea" — and when it does, setResults silently overwrites the correct result with the stale one. The component shows the wrong data. No error, no warning, no clue. This is a race condition, not a hypothetical. It happens on slow networks, during fast typing, on underpowered devices, and in staging environments right before a demo. AbortController: the three-line fix An AbortController is a pair: a controller object and a signal. You pass the signal into any abort-aware API; you call abort() to cancel it. useEffect (() => { const controller = new AbortController (); fetch ( `/api/search?q= ${ query } ` , { signal : control

Tra performance, piattaforma e toolchain: i temi che contano davvero per chi costruisce frontend oggi. Il frontend nel 2026 è diventato una disciplina sempre più “di prodotto”: non basta far funzionare l’interfaccia, serve che sia veloce, stabile, accessibile e misurabile in produzione. E quando l’ecosistema Chrome parla di “connessione” tra developer e piattaforma, il messaggio utile per chi lavora sul web è semplice: capire dove investire tempo per ottenere impatto reale sugli utenti . Di seguito, una lettura pratica dei temi che continuano a emergere come prioritari per chi costruisce applicazioni e siti moderni. 1) Performance: meno benchmark, più realtà La performance non è più un esercizio di ottimizzazione a fine progetto. È un requisito continuo che va gestito con strumenti, metriche e processi. Cosa significa “misurabile” oggi Metriche di campo (real user monitoring) : le prestazioni che contano sono quelle che arrivano dai dispositivi reali, su reti reali. Metriche di laboratorio : restano utili per regressioni e CI, ma vanno interpretate come “segnali” e non come verità assolute. Implicazione pratica Imposta una pipeline dove: le metriche sintetiche bloccano regressioni evidenti (build/PR), le metriche reali guidano le priorità (release e backlog). 2) DevTools: dal debug al controllo qualità Gli strumenti di sviluppo non servono più solo a “trovare il bug”, ma a ridurre il rischio : regressioni di layout, memory leak, risorse inutili, dipendenze pesanti. Abitudini che fanno differenza Profilare prima di ottimizzare: CPU, rete e rendering hanno colli di bottiglia diversi. Isolare i cambiamenti: una variazione di bundling o di immagini può ribaltare il profilo prestazionale più di una micro-ottimizzazione in JS. 3) La piattaforma web continua a crescere (e chiede scelte più consapevoli) La Web Platform oggi offre API potenti, ma la parte difficile non è “usarle”: è scegliere quando usarle. Un criterio utile Se una feature riduce complessità (meno librerie,

SaaS software vendor HubSpot has described how its semantic search platform grew from a proof of concept into an internal service that now manages more than 20 billion vectors across 38-plus teams. The company says the system now supports agents, RAG, and contact deduplication, and that the increase in agent usage has made retrieval quality and latency more important than before. By Matt Saunders

When people talk about hackathons, they talk about the demo. The pitch, the UI, the "aha" moment on stage. Nobody really talks about the person who spent the whole weekend making sure the data didn't fall apart. That was me on AidStream, a blockchain-based aid distribution platform we built in a weekend and trust me it wasnt that easy as it seems. At a normal project, you can revisit your data model whenever.But during a hackathon you cant since when teamamtes start working on top of your tables it means both codes may start breaking . Serverless Postgres was the right choice for a hackathon: no local DB setup, no "wait, whose laptop has Postgres installed" problem. Everyone could connect to the same instance immediately. The gotcha was connection limits — with multiple people hitting the same database while testing features simultaneously, we ran into connection issues at the worst possible time (an hour before demo). So next time you watch a hackathon demo go off without a hitch, remember — someone probably spent the whole weekend quietly making sure the database didn't have a say in it. If you're the one holding the schema together at 2am, know this — it's not the flashy role, but it's the one that decides whether anyone else's code even runs.

Node.js 26 has been released, featuring the Temporal API enabled by default, an updated V8 engine to version 14.6, and the Undici HTTP client upgraded to 8.0. The release also removes deprecated legacy APIs. Developers should note migration points related to NODE_MODULE_VERSION changes. Node.js 26 is current for six months before entering long-term support. By Daniel Curtis

I was implementing a feature where the session container would track a lastActivity timestamp, updated on every authenticated request. Standard stuff. I wrote it, tested it locally with curl, and noticed something odd: I kept getting a new Set-Cookie header value on every response. Not occasionally. On every single one. A week later I was sending a pull request to mezzio/mezzio-session-cache . The Setup: Two Backends, One Session Our system had a constraint: two backend applications, written in different languages, sharing a single user session. One was the main PHP/Mezzio app. The other was a service in a different stack that needed to read from, and update the lastActivity timestamp on, the same session container. There are a few ways to make polyglot session sharing work. We landed on a shared cache backend (Redis) with a well-defined session structure. Both apps could read and write through their own libraries, as long as they agreed on the storage format and the cookie name. The session ID was the contract. That contract is the part that quietly broke. A Missing Escape Hatch My first instinct was the usual list of suspects. Was something calling regenerateId() in a middleware I didn't know about? Was there a logout being triggered somehow? Was a misconfigured cache layer evicting and recreating sessions? After a bit of digging through the call stack, I ended up in the library itself. And there it was: CacheSessionPersistence was regenerating the session ID whenever the session data changed . Not on login. Not on privilege escalation. On every write . That's when the real question hit me: why on earth would a library do that by default? Reading Code Before Changing It When you find behavior that surprises you in someone else's code, the wrong move is to immediately label it broken. The right move is to assume the maintainers had a reason, and find out what it was. The reason, in this case, is session fixation . Session fixation is a class of attack where an atta

For years, developers have faced the same dilemma when implementing complex search APIs: GET is the correct semantic choice for read-only operations, but query parameters can become extremely long and difficult to manage. POST allows sending a request body, but it's intended for operations that may change server state, making it a poor semantic fit for searches. To bridge this gap, the IETF has introduced a new HTTP method: QUERY (RFC 10008). Why was QUERY introduced? Modern APIs often require complex filtering: nested JSON filters GraphQL-like requests advanced search criteria large lists of IDs geospatial or analytical queries Encoding all of this into a URL is cumbersome and can exceed practical URI length limits. Developers have traditionally worked around this by using "POST" for read-only searches. The problem is that "POST" doesn't express the intent of the request very well. The new QUERY method solves this by allowing clients to send a request body while keeping the operation explicitly safe and idempotent. Key benefits ✅ Request body support Unlike "GET", "QUERY" allows sending structured request data in the message body, making complex searches much easier to model. ✅ Safe by design Like "GET", a "QUERY" request must not modify server state. It clearly communicates that the request is read-only. ✅ Idempotent Repeating the same "QUERY" request produces the same result without additional side effects, allowing clients and intermediaries to safely retry requests after transient failures. ✅ Cache-friendly Unlike the common "POST"-for-search pattern, "QUERY" is designed to work with HTTP caching, enabling better performance and more efficient network usage. ✅ Better API semantics Instead of overloading "POST" for read operations, APIs can now express their intent more accurately: "GET" → simple resource retrieval "QUERY" → complex read operations with a request body "POST" → operations that create or modify state Example Instead of forcing everything into a lo

Python's Memory Model Is Not What You Think It Is Ask most Python developers how Python stores a variable and they will say "it stores the value." This is imprecise in a way that causes real bugs and real confusion in interviews. A precise mental model of how Python stores and retrieves data changes how you read and write code. Python does not store values in variables. Python binds names to objects. The distinction sounds philosophical until you trace code that involves mutation, function arguments, or aliasing. Then it becomes the most practically useful concept in the language. Names Are Not Boxes The box metaphor, which says a variable is a box that holds a value, is how most introductory programming courses explain variables. In many languages this metaphor is close enough to accurate that it does not cause problems. In Python it is wrong in ways that matter. A more accurate metaphor: a Python name is a label attached to an object. The object exists independently in memory. Multiple labels can be attached to the same object. Attaching a new label does not move or copy the object. x = [ 1 , 2 , 3 ] y = x print ( id ( x ) == id ( y )) # True (same object, two labels) When you write y = x , you are not copying the list. You are creating a second label that points to the exact same list object. The Four Operations You Must Distinguish 1. Assignment creates a new binding x = [ 1 , 2 , 3 ] x = [ 4 , 5 , 6 ] # x now labels a completely different object The first list still exists in memory until garbage collected. The name x simply stops pointing to it and now points to the second list. 2. Mutation modifies an existing object x = [ 1 , 2 , 3 ] x . append ( 4 ) # the object x labels is modified in place Any other name pointing to the same object will instantly reflect this change because they look at the same memory location. 3. Augmented assignment on mutable types mutates x = [ 1 , 2 , 3 ] y = x x += [ 4 , 5 ] print ( y ) # [1, 2, 3, 4, 5] (same object, mutated) The