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Category theory often sounds like impenetrable academic jargon, but at its core, it is simply the mathematics of composition. In functional programming, we use its concepts to build modular, predictable, and bug-resistant code. JavaScript isn't a pure functional language like Haskell, but it has first-class functions and treats functions as values. This makes it entirely possible to implement category theory primitives. Here is how the theoretical concepts map to practical JavaScript. The Vocabulary of Category Theory Before jumping into code, let's define the fundamental pieces: Categories: A collection of objects and arrows (morphisms) between them. Objects: In programming, these are our types or data (e.g., String, Number, Boolean, Array). Morphisms: These are our pure functions that transform one type into another (e.g., a function that takes a String and returns a Number). The goal of functional programming primitives is to create safe wrappers (containers) around our data so we can compose these functions predictably, without side effects or unhandled errors. 1. Functors: The Mappables A Functor is any type that implements a map method. It is a container holding a value, and map allows you to apply a function to that value without pulling it out of the container. When you map over a Functor, it returns a new Functor of the same type, preserving the container's structure. The native JS Functor: You already use Functors every day. JavaScript Arrays are Functors. const numbers = [ 1 , 2 , 3 ]; // We apply a function to the values inside, and get a new Array back const doubled = numbers . map ( x => x * 2 ); // [2, 4, 6] Building a custom Functor: Let's build a simple Box container to see how this works for single values. const Box = x => ({ map : f => Box ( f ( x )), fold : f => f ( x ), // An escape hatch to get the value out inspect : () => `Box( ${ x } )` }); // Usage: const result = Box ( ' Functional Programming ' ) . map ( str => str . trim ()) . map ( str

Before I critique modern web development, I want to make some points: I'm not discouraging anybody from web development. This is only my opinion and my experience of the story. Everyone has their own story. Part 1. Javascript JavaScript is known for being the web's language. It's used everywhere in the web, and can also be used to make external applications outside of web development. JavaScript has many flaws in its language, such as its comparison logic. JavaScript uses the "strict equality operator" that tries to solve its weird type coercions (e.g [] == ![] being true). Another flaw is that JavaScript has poor maintainability. JavaScript has a poorly managed type system that introduces errors without proper error handling. You cannot explicitly define what error you're trying to catch in the try-catch statement. try { functionThatThrowsError () } catch ( error ) { console . error ( error ); if ( error instanceof MyError ) { doSomething (); } } instead of modern languages: try { functionThatThrowsError () } catch ( e : MyError ) { print ! ( " error: ${e.message} " ) } This adds complexity to try-catch statements and makes it almost impossible to handle errors in a robust manner. Another issue with JavaScript's maintainability is the wording of the language. JavaScript has two keywords called undefined and null . In practice these should mean the same thing but it doesn't. An undefined variable is an uninitialized variable, or a function that doesn't return anything. Undefined: // Returns undefined function getName () {} // syntactically correct in JavaScript // but will cause undefined console . log ( getName ()); const myCoolObject = {}; console . log ( myCoolObject . property ); // still syntactically correct in JavaScript even though the property doesn't exist. Null: // Returns a null name function getName () { return null ; } // output: null console . log ( getName ()); const myCoolObject = { property : null }; console . log ( myCoolObject . property ); //out

AxonASP is a high-performance Classic ASP engine written in Go — but here's the twist: it runs JavaScript (JScript) natively on the server side. You get a synchronous, predictable execution model, full ECMAScript 5/6+ support, and zero dependency on Node.js or any third-party JavaScript runtime. And yes — you can build an entire production website with it. Why AxonASP Changes the Game for Server-Side JS Most developers associate server-side JavaScript exclusively with Node.js. And Node.js is great — until you're drowning in async/await chains, package.json conflicts, and the 47th minor version bump of a dependency that broke your build. AxonASP takes a fundamentally different approach. Instead of wrapping everything in an event loop and forcing asynchronous patterns everywhere, AxonASP's JavaScript engine executes code synchronously by default . You write your server logic the same way you write your frontend logic — line by line, top to bottom. It compiles through a high-performance AST parser and runs directly on a custom Go-based virtual machine. The result? Cleaner code, simpler debugging, and a massive reduction in cognitive overhead. What You Get Out of the Box Full ES5 + ES6 support (classes, arrow functions, template literals, destructuring, proxies, for...of , Map , Set , Symbol , typed arrays — 37+ modern features documented) Synchronous execution — no callback pyramid, no promise chains for basic I/O ASP intrinsic objects — Request , Response , Session , Application , Server — all accessible directly from your JS code No npm install required — just write .asp or .js files and point the server at them CLI execution — run JavaScript files from the command line for automation, batch processing, or testing The Philosophy: Simplicity Over Complexity Here's a hard truth: most web applications don't need 2,000 npm modules. They need to read a database, render HTML, handle form submissions, and maybe serve a JSON API. That's it. The modern JavaScript ecosystem ha

Background Take Lombok as an example: at compile time, it reads annotations like @Getter and @Setter through an Annotation Processor (APT, Annotation Processing Tool), then directly modifies the AST (Abstract Syntax Tree) in memory, injecting getter/setter methods for fields. Developers only need to annotate fields, and the compiled .class files automatically include these methods. Advantages of APT APT is an extension mechanism natively supported by the Java compiler. Annotation processors run at compile time and can read annotation information from source code to generate new source files. Its advantages are: Standardized — Simply implement the Processor interface; no need to hack the compiler Composable — Multiple annotation processors can work together in the same compilation process Generated code is visible — Source files generated through the Filer API are located in the build/generated/ directory, and IDEs can navigate to them after configuring the source path Disadvantages of APT APT has a fundamental limitation: it cannot generate a class with the same fully qualified name as the source code. The Java compiler explicitly specifies that two classes with the same fully qualified name are not allowed in the same compilation unit. Source code generated by APT participates in the same round of compilation as the original source code, so it can never generate a class with the same fully qualified name as the original class. This means APT cannot truly "modify" a class; it can only generate new classes (such as Builder, Factory, etc.). Advantages of AST Tools like Lombok bypass the APT limitation by directly modifying the AST in the compiler's memory — injecting methods for fields, injecting constructors for classes, etc. The advantages of AST modification are: Can modify existing classes — Methods are added directly to the AST in memory, breaking through the limitation that APT cannot generate Same-Name Classes Zero intrusion — Classes written by developers rema

GitHub热门项目 | genshijin 原始人 🗿| Claude Code / Codex等AIエージェント 向け超圧縮コミュニケーションスキル。caveman の日本語版をベースに、日本語特有の冗長表現に最適化。 | Stars: 257 | 6 stars today | 语言: JavaScript

The problem with "just add another tool" When you're building one image tool, performance is easy. When you're building 71 of them in the same app — resize, compress, crop, PDF merge, format converters, exam-photo presets, social media templates — the naive approach (import everything, bundle it all together) turns your app into a multi-megabyte JavaScript payload before a user has even picked a tool. This is the actual engineering problem behind ResizeHub , which now has 71+ tools across 11 categories, all running client-side with zero server uploads. Here's how the architecture holds up at that scale. Stack, and why each piece earns its place React + TypeScript — type safety matters more, not less, as tool count grows. A shared ImageProcessor interface that every tool implements catches integration bugs at compile time instead of in production. Vite — its native ES modules dev server and Rollup-based production build made code-splitting dramatically easier to reason about than older bundlers, which matters a lot once you have dozens of independent tool routes. HTML5 Canvas API — the actual compression/resize/crop engine, shared across tools rather than reimplemented per-tool. Cropper.js — for interactive cropping UI specifically (aspect-ratio locking, circular crop for signatures) rather than rebuilding drag-handle math from scratch. Pica — for high-quality image downscaling; the browser's native canvas scaling can introduce visible aliasing on large downscales, and Pica's algorithm handles this noticeably better. Cloudflare Pages — static hosting with edge caching, which matters since 100% of the actual processing work happens in the user's browser, not on any server at all. Lesson 1: Route-level code splitting isn't optional past a handful of tools With React Router and dynamic import() , each tool becomes its own chunk: const PhotoResizer = lazy (() => import ( ' ./tools/PhotoResizer ' )); const PdfCompressor = lazy (() => import ( ' ./tools/PdfCompressor ' ));

You have a Java application, and now you need it to do something Java is awkward at. Maybe you want to let users script your app without recompiling it. Maybe you want to change a piece of business logic without a full redeploy. Maybe you just want to run a quick, throwaway script and not stand up a whole build. Java is a phenomenal language and runtime, but these are the edges where it starts to feel heavy. That is exactly the space Aussom is built for. I started using Java almost 20 years ago and have loved every bit of it. I'm proud of all the recent momentum in the Java space and I hope it continues. Java does so much so well. But every great tool has an edge where it stops being the right one, and reaching for another language there isn't a betrayal of Java. It's how the best ecosystems work. A useful comparison: C and Python Look at C. C is clearly important. Even after a lifetime of use it's still the foundation for new projects today, and new competitors such as Rust haven't been able to meaningfully displace it. C is fast and powerful on its own, but it isn't great for simple tasks. It's poor for throwaway code and quick scripts, it isn't very portable, and it hands you plenty of footguns. It's also a poor choice when you want to offer a scripting interface. Enter Python. Python is everywhere today because the barrier to entry is so low and it's genuinely useful. But Python leans on C. It's written in C, and much of its power comes from existing C libraries, whether they're UI frameworks, AI inference engines, or anything in between. C is efficient but poor at simple dynamic work; Python is dynamic and simple but poor at raw power and efficiency. Neither replaced the other. They endure together because they complement each other's weaknesses. That is the case I make for Aussom. Aussom is to Java as Python is to C. It doesn't compete with Java, it complements it. What makes Aussom different from other JVM languages This is where Aussom parts ways with most o

Every developer who has worked with LLMs has been there. You ask the model for JSON. You describe the schema. You say "please only respond with valid JSON." And sometimes, it still breaks. Your application crashes because the model returned a string where you expected an integer. Or it wrapped the JSON in markdown code blocks. Or it omitted a required field. Spring AI 2.0 has a solution that treats this like a real engineering problem instead of a prayer. The Problem When you use structured output in Spring AI, the workflow goes like this: You define a Java type (a record, class, or enum) Spring AI generates a JSON schema from that type The schema gets appended to the prompt sent to the LLM The model returns a response Spring AI attempts to deserialize the response into your type This works well with frontier models like Claude and GPT-4. But smaller open-source models, like Llama 3.2 1B running locally via Ollama, fail more often. They might return null for a primitive field, omit required fields, or produce malformed JSON. When it fails, you get a deserialization exception. Your endpoint returns a 500 error. Spring AI provides no built-in recovery mechanism. The Old Approach: Hope Consider a conference talk submission system. Speakers submit messy, unstructured abstracts. You want to extract structured data: public record TalkSubmission ( String title , String abstractText , Level level , // BEGINNER, INTERMEDIATE, ADVANCED Track track , int duration , List < String > tags , String speakerHandle ) {} Here is what the basic typed response looks like: @PostMapping ( "/typed" ) public TalkSubmission typed ( @RequestBody String rawSubmission ) { return chatClient . prompt () . system ( systemPrompt ) . user ( spec -> spec . text ( "Extract the talk submission: {submission}" ) . param ( "submission" , rawSubmission )) . call () . entity ( TalkSubmission . class ); } You define your type. Spring AI generates the schema and appends it to the prompt. The model gets the in

Frontend testing used to sound simple: open a page, find an element, click it, and verify the result. That description still works for basic workflows, but modern interfaces are no longer a single static DOM that changes in obvious ways. Components can render inside Shadow DOM. Modals can be portaled to a different part of the document. Server-rendered HTML can be replaced during hydration. Content can move because of CSS container queries. A page can look finished while several progressive-loading states are still changing underneath it. The hardest frontend bugs now tend to sit at the intersection of three things: State: what the application believes is happening. Timing: when the browser and framework apply changes. Geometry: where elements appear and whether users can actually interact with them. A stable test strategy has to observe all three. Shadow DOM and portals break naive assumptions about element location Component encapsulation is useful, but it changes how automation finds and interacts with elements. A control inside an open shadow root is not always reachable through the same selector strategy used for the main document. A portaled modal may appear visually next to a component while being rendered near the end of document.body . Focus can move into the modal even though the DOM hierarchy suggests that it belongs elsewhere. This guide to testing Shadow DOM and portaled modals without breaking browser automation suites covers the key challenges. The test should verify behavior, not merely the presence of a node: Can the user reach the control? Is the expected element visible above overlays? Does keyboard focus enter the modal? Is focus trapped correctly? Does Escape close it? Does focus return to the triggering element? Can a screen reader identify its label and role? Selectors still matter, but interaction boundaries matter more. A test that locates a button hidden behind another layer is not testing what the user experiences. Hydration creates a peri

Installing Apache Kafka 4.2 on Ubuntu 24.04 (WSL2) Using KRaft Mode: A Complete Step-by-Step Guide Learn how to install Apache Kafka 4.2 in KRaft mode, understand its architecture, create topics, produce and consume messages, and troubleshoot common configuration issues—all without ZooKeeper. 🚀 Introduction Apache Kafka has become the de facto standard for building event-driven , real-time , and high-throughput applications. Whether you're processing millions of financial transactions, collecting application logs, streaming IoT sensor data, or connecting microservices, Kafka provides a scalable and reliable messaging platform. Until recently, setting up Kafka required running Apache ZooKeeper alongside Kafka brokers. While powerful, ZooKeeper added operational complexity and introduced another distributed system that administrators had to manage. Beginning with recent Kafka releases, KRaft (Kafka Raft Metadata mode) removes this dependency by allowing Kafka to manage its own metadata internally. This makes installation simpler, reduces operational overhead, and improves scalability. In this guide, we'll install Apache Kafka 4.2 on Ubuntu 24.04.4 LTS (WSL2) , configure a single-node KRaft cluster, and walk through the complete lifecycle: Installing Kafka Understanding the Kafka architecture Configuring KRaft mode Starting the broker Creating topics Producing and consuming messages Troubleshooting common issues Understanding the purpose of each configuration parameter Rather than simply listing commands, I'll explain why each step is necessary so that you understand how Kafka works under the hood. What is Apache Kafka? Apache Kafka is a distributed event streaming platform designed to move data reliably and efficiently between applications. Instead of applications communicating directly with each other, they communicate through Kafka. A producing application writes messages to Kafka. Kafka stores those messages reliably. One or more consuming applications read those m

After the browser transfers a file, the server may still scan, transcode, extract metadata, or generate previews. The interface needs status updates, but the most fashionable real-time transport is not automatically the most reliable choice for an event guest on a mobile browser. Start with the update contract Define states and transitions before choosing transport: accepted -> queued -> processing -> ready accepted -> rejected processing -> processing_error Every status response should include a monotonically increasing version or timestamp. The client can ignore events older than the state it already knows. Polling is a strong baseline HTTP polling works through proxies, resumes naturally after a page wake, and is easy to cache and rate-limit. Use adaptive intervals: one second just after acceptance, then back off to several seconds when processing takes longer. const delay = Math . min ( 8000 , 1000 * 2 ** slowChecks ); Add jitter when many guests finish at the same time. Stop when the state is terminal, the page is gone, or a server-provided retry time says to wait longer. SSE fits one-way progress Server-Sent Events are attractive when the server only pushes status. The browser reconnects with a last-event ID, and the wire format is simple. But mobile networks and some intermediaries silently kill idle connections. Send occasional heartbeats and treat reconnection as normal. The reconnect handler must fetch current state because events may have been missed beyond the server replay window. Do not keep one SSE stream per file. Subscribe by guest session or album and filter authorized upload IDs on the server. WebSockets add more responsibility WebSockets make sense when the client also sends frequent commands over the same channel or when a host moderation console needs many rapid updates. For a guest waiting on two files, they add connection authentication, heartbeat, reconnect, replay, and load-balancer complexity without much user benefit. Never rely on the so

Signed upload sessions expire. Event contribution windows close. Storage reservations are reclaimed. These are normal lifecycle events, but many interfaces reduce all of them to “Upload failed.” A better protocol makes expiration explicit and recoverable where policy allows it. Distinguish three clocks An upload workflow usually has at least three deadlines: the event contribution window; the server-side upload intent expiry; the short-lived storage credential expiry. They should not share one timestamp. The event may accept contributions until midnight while a credential lasts five minutes and an intent can be renewed for an hour. Return the clocks in the session response with server time: { "serverNow" : "2026-07-17T18:00:00Z" , "eventClosesAt" : "2026-07-18T00:00:00Z" , "intentExpiresAt" : "2026-07-17T19:00:00Z" , "credentialExpiresAt" : "2026-07-17T18:05:00Z" } The client can display useful warnings without trusting its own clock for authorization. Model expiration by state A credential expiring during transfer is different from an intent expiring before completion. Use stable reasons: credential_expired -> request renewal intent_expired -> reconcile committed parts, then renew or restart event_closed -> stop new work, preserve local recovery guidance Do not automatically restart from byte zero. First ask the control plane which parts were committed and whether the original object key remains valid. Renew narrowly A renewal endpoint should accept the upload ID and prove continuity with the guest session. It rechecks event state, file policy, rate limits, and committed size. The response returns a new credential for the same object. Do not let the browser extend an intent indefinitely. Define a maximum session age and a bounded number of renewals. Long uploads may receive a larger initial policy based on file size and observed throughput. Handle the closing boundary fairly Decide what happens to an upload already transferring when the event window closes. Reasona

Client-side media preflight can improve an upload experience, but it can also crash the page before the network request begins. A twelve-megapixel JPEG may be only four megabytes on disk and tens of megabytes after decoding. Creating several full-size canvases at once is enough to exhaust memory on older phones. Preflight is policy, not editing Decide what the browser must prove before transfer. Useful checks include file count, compressed byte size, declared type, readable dimensions, and a conservative duration limit for video. Avoid mandatory re-encoding unless the product truly needs it. Every transformation adds CPU time, memory pressure, battery use, and another failure mode. Process one file at a time A file picker may return twenty items. Do not decode all of them to build previews. Maintain a queue with one active decode on constrained devices and at most two on stronger ones. for ( const file of files ) { const result = await inspect ( file ); renderResult ( result ); await yieldToMainThread (); } The UI can list filenames immediately while dimensions and thumbnails arrive progressively. Prefer metadata over full pixels Use createImageBitmap with resize hints when supported. It can decode away from the main rendering path and avoid a full-resolution canvas for a small preview. const bitmap = await createImageBitmap ( file , { resizeWidth : 640 , resizeQuality : ' medium ' }); Always close bitmaps after drawing. Revoke object URLs when the component unmounts. Small leaks become large when guests select and remove files repeatedly. Handle orientation and color carefully Modern browsers generally honor EXIF orientation when decoding, but behavior differs across APIs and older engines. Test portrait photographs from real iOS and Android devices. Do not strip metadata silently if the original is supposed to remain untouched; upload the source file and treat the preview as disposable UI. Color differences between the preview and exported original are usually les

This is Part 8 of 10 , a bonus practice article with 70 code-output challenges . Each challenge asks you to predict the result before revealing the answer and reasoning. Complete series This Dev.to series has five core handbook articles plus five focused practice extras. Open the series page to move through the complete reading order: Part 1: JavaScript — core handbook, questions 1–120 Part 2: React — core handbook, questions 121–220 Part 3: React Native — core handbook, questions 221–420 Part 4: Performance & Architecture — core handbook, questions 421–560 Part 5: Senior & System Design — core handbook, questions 561–719 Part 6: Output-Based JavaScript Practice — bonus practice article Part 7: Coding Interview Practice — bonus practice article Part 8: Code Output Challenges — bonus practice article Part 9: Current React Native Interview Questions — new high-frequency practice article Part 10: Project & Production Interviews — senior project ownership and real-production practice How to use this challenge set Read the code, state the exact output or error, then explain the language rule. Do not run the snippet until you have committed to an answer. For React Native interviews, connect the JavaScript behavior to rendering, state updates, list handling, or the JavaScript thread when relevant. Skills tested Hoisting, scope, closures, and this Arrays, conditions, references, object behavior, and loose versus strict equality Promises, timers, async / await , and microtasks Common JavaScript patterns used in React and React Native interviews Code output challenges Challenge 1. Block-scoped counter Predict the exact output before opening the answer. let total = 0 ; for ( let i = 0 ; i < 3 ; i ++ ) { total += i ; } console . log ( total ); Answer and explanation Expected output: 3 Why: The loop adds 0, 1, and 2. Challenge 2. var callback loop Predict the exact output before opening the answer. for ( var i = 0 ; i < 3 ; i ++ ) { setTimeout (() => { console . log ( i ); }, 0

Event uploads fail differently from normal office uploads. A wedding guest may move between venue Wi-Fi and mobile data, lock the phone while a video is transferring, or close the browser as soon as the progress bar reaches 100%. Hundreds of devices can share one access point, and users rarely wait around to diagnose an error. The usual POST request plus optimistic success toast is not enough. A reliable browser flow needs a small protocol that distinguishes local preparation, network transfer, server acceptance, media processing, and final availability. This article describes a platform-neutral design for that protocol. The five states users actually experience Model each file as a durable state machine: selected -> preparing -> transferring -> accepted -> processing -> ready Add terminal or recoverable branches: preparing -> rejected_local transferring -> paused | retryable_error | expired accepted -> processing_error processing -> ready | processing_error The key distinction is between transferring and accepted . The browser may have sent every byte while the server has not yet committed the upload. Showing “done” at that boundary creates the most frustrating failure: the guest deletes the original, but the organizer never receives it. Give every file a client-generated identity Create an upload ID before the first network request. A UUID is sufficient when combined with the event identifier: const uploadId = crypto . randomUUID (); const uploadIntent = { uploadId , eventId , name : file . name , size : file . size , type : file . type , lastModified : file . lastModified , }; Send that identity when creating the server-side upload session. If the browser retries after a timeout, the server returns the existing session instead of creating a duplicate. This is idempotency at the workflow level. A guest can tap “retry” without having to understand whether the first request reached the server. Separate the control plane from file bytes Use a small JSON API for sessi

This is Part 7 of 10 , a bonus practice article containing 75 coding interview questions drawn from the React Native Interview Handbook. It covers the implementation tasks commonly used in JavaScript and React Native rounds, from string and array problems to hooks, FlatList , asynchronous work, caching, retries, and native modules. Complete series This Dev.to series has five core handbook articles plus five focused practice extras. Open the series page to move through the complete reading order: Part 1: JavaScript — core handbook, questions 1–120 Part 2: React — core handbook, questions 121–220 Part 3: React Native — core handbook, questions 221–420 Part 4: Performance & Architecture — core handbook, questions 421–560 Part 5: Senior & System Design — core handbook, questions 561–719 Part 6: Output-Based JavaScript Practice — bonus practice article Part 7: Coding Interview Practice — bonus practice article Part 8: Code Output Challenges — bonus practice article Part 9: Current React Native Interview Questions — new high-frequency practice article Part 10: Project & Production Interviews — senior project ownership and real-production practice How to answer coding questions Before coding, clarify inputs, output, edge cases, platform constraints, time complexity, space complexity, cancellation, and test coverage. Start with a correct readable solution, then optimize only when the constraint justifies it. Topics covered Strings, arrays, maps, sets, recursion, and algorithmic complexity Debounce, throttle, memoization, deep cloning, and polyfills Custom hooks, API state, error boundaries, and React rendering FlatList pagination, pull to refresh, search, and offline retry Promises, timeouts, concurrency limits, and exponential backoff Caching, EventEmitter, Pub/Sub, LRU design, and native module boundaries Interview coding checklist Confirm assumptions before writing code. State time and space complexity. Handle empty input, invalid input, and duplicate values deliberately

This is Part 6 of 10 , a bonus practice article containing 191 output-based JavaScript interview questions . It includes 111 questions drawn from the React Native Interview Handbook plus 80 additional questions on the JavaScript behavior interviewers commonly test in React Native rounds: hoisting, scope, closures, arrays, objects, functions, coercion, conditions, Promises, and the event loop. Complete series This Dev.to series has five core handbook articles plus five focused practice extras. Open the series page to move through the complete reading order: Part 1: JavaScript — core handbook, questions 1–120 Part 2: React — core handbook, questions 121–220 Part 3: React Native — core handbook, questions 221–420 Part 4: Performance & Architecture — core handbook, questions 421–560 Part 5: Senior & System Design — core handbook, questions 561–719 Part 6: Output-Based JavaScript Practice — bonus practice article Part 7: Coding Interview Practice — bonus practice article Part 8: Code Output Challenges — bonus practice article Part 9: Current React Native Interview Questions — new high-frequency practice article Part 10: Project & Production Interviews — senior project ownership and real-production practice How to use this guide Before opening an answer, state the exact output first. Then explain the rule that causes it: evaluation order, scope, coercion, reference identity, prototype lookup, or microtask scheduling. Run the snippet only after committing to an answer. Topics covered Hoisting, Temporal Dead Zone, var , let , const , and function declarations Scope, closures, this , arrow functions, call , apply , and bind Arrays, sparse arrays, map , reduce , sort , slice , splice , and mutation Objects, references, shallow copies, prototypes, getters, and property lookup Conditions, truthiness, equality, nullish coalescing, and type coercion Promises, async / await , microtasks, timers, and error recovery React and React Native rendering behavior, state updates, effects,

I created the official PasteDB VS Code extension that lets you upload your current file or selected code directly from Visual Studio Code. It includes secure API key storage, customizable upload settings, recent paste history, automatic language detection, and instant shareable links. In this post, I'll explain how it works, how to install it, and the challenges I faced while building it. More Info View on Marketplace

GitHub热门项目 | A browser based code editor | Stars: 46,370 | 10 stars today | 语言: JavaScript

GitHub热门项目 | a free and opensource app that lets you gain an unfair advantage | Stars: 5,473 | 57 stars today | 语言: JavaScript