标签：#java

GitHub热门项目 | List of Permanent Free LLM API (API Keys) | Stars: 4,786 | 32 stars today | 语言: JavaScript

GitHub热门项目 | Native web workspace for Hermes Agent — chat, terminal, memory, skills, inspector. | Stars: 5,313 | 63 stars today | 语言: JavaScript

GitHub热门项目 | PDF Parser for AI-ready data. Automate PDF accessibility. Open-source. | Stars: 23,160 | 573 stars today | 语言: Java

The message looked completely normal. A recruiter, a short pitch, a "take-home challenge" hosted on GitHub. Clone it, run npm install , get the dev server up, build a small feature, send it back. Standard stuff. I have done a dozen of these. This one was trying to steal my wallet keys and browser session data before I ever wrote a line of code. It did not hide the malware in the app. It hid it in the build tooling. That is the whole trick, and it is the reason a lot of experienced developers get caught. You read src/ , it looks fine, so you trust it. Nobody reads the lockfile. Nobody reads the postinstall script. That is exactly where the payload lives. Here is the full teardown: what the lure looks like, the exact red flags, how I investigated it without running it, and the defenses you should adopt today. The setup: Contagious Interview This is a known campaign. Security researchers track it as "Contagious Interview," attributed to North Korea-aligned actors. The pattern is consistent: You get contacted about a job, often blockchain or full-stack, often with a salary that is a little too good. You are given a code repository to clone and run as a "technical assessment." The repo runs malicious code at install or build time, not at runtime. The payload pulls a second-stage downloader, grabs your environment variables, crypto wallet files, browser-stored credentials, and keychain data, then exfiltrates them to a remote host. The genius of it is the framing. A normal developer reflex when running untrusted code is "I will read the code before I trust it." But you read the application code. You do not read what npm install does, because npm install is something you run a hundred times a week without thinking. Red flag 1: a postinstall script that does not belong The first thing I do with any unfamiliar repo is open package.json and read the scripts block. Specifically, I look for lifecycle hooks: preinstall , install , postinstall , prepare . These run automatically w

React + Mapbox GL JS: Custom Markers, Popups, and Bounds-Based Data Fetching If you've added a Mapbox map in a React app before, you've probably hit a wall pretty quickly: Mapbox GL JS manages its own DOM, and React manages a virtual DOM, and getting the two to play nicely takes some thought. Markers and popups are a great example of this tension — they're imperative APIs in a world where you want declarative components. This post walks through a pattern I've landed on for wrapping mapboxgl.Marker and mapboxgl.Popup in composable React components, and combining them with bounds-based data fetching to build something like a real estate or earthquake explorer map — where the data on the map updates as you pan and zoom. Here's what we'll cover: Wrapping mapboxgl.Marker in a React component that handles its own lifecycle Using createPortal to render custom marker content in React Fetching data from an API using the map's current bounding box Tracking an active marker with React state Wrapping mapboxgl.Popup in a component If you want to see the finished product first, the source code is on GitHub . The Core Challenge: Two DOMs Before we get into the code, it's worth understanding why this requires a pattern at all. When you use React's normal component tree, React controls the DOM. But mapboxgl.Marker also creates and manages DOM nodes — it places an element on the map at a specific geographic coordinate, handles repositioning as you pan, and removes itself cleanly when you're done with it. The approach here is to use React to manage the lifecycle of each marker (mount when data arrives, unmount when data goes away), while letting Mapbox handle the positioning . We use React's createPortal to render JSX content into a DOM node that we hand off to Mapbox. The example discussed below is a map that fetches earthquake data for the current viewport, displaying a custom Marker and Popup for each. As you move the map and the bounds change, new data is fetched and the Markers a

A client asked me for a simple thing. Not ChatGPT. Not an agent. Not a multimodal assistant that can explain invoices, generate React components, and write poetry in three languages. Just a small classifier embedded into a website. The job sounded boring in the best possible way: take some text, classify it, return a result, keep it fast. So I started looking at the usual solutions. And then I had one of those moments where you stop reading documentation, lean back, and ask: Are we seriously doing this? Because the answer I kept running into looked like this: download a huge runtime download a huge model initialize a big ML stack then classify one small piece of text In one setup, the path was getting close to something like 250 MB per user . For a simple classifier. On a website. From a server. Every time. No. Sorry. That is insane. The problem The web has a strange habit now. You ask for one small AI feature, and the answer is often: bring the entire construction company. But sometimes I do not need a construction company. I need one person on the construction site. One task. One tool. One result. This is especially true for simple classification, embeddings, semantic search, routing, filtering, ranking, small local decisions. Not every AI problem needs an LLM. Not every website needs a full inference engine. Not every user should pay a 250 MB download tax because we were too lazy to think smaller. So I started digging I wanted something simple: runs in the browser does not require a server for inference small enough to actually ship works with transformer-style models can tokenize text can run BERT-like forward inference can produce embeddings or classification input does not bring ONNX Runtime, Candle, ndarray, or half the internet with it At first I thought: “Surely someone already made the tiny version.” There are great tools out there. Transformers.js is powerful. ONNX Runtime Web is powerful. Candle is powerful. But that was exactly the problem. They are pow

Hello Dev Community! 👋 It is officially Day 12 of my journey to master the MERN stack! Today, I wrapped up Lecture 3 of Apna College's JavaScript playlist with Shradha Didi, focusing on a fundamental data type we use every day: Strings . Before today, I thought strings were just plain text wrapped in quotes. Today, I learned how much power JavaScript gives us to manipulate, slice, and dynamically format text. 🧠 Key Learnings From JS Lecture 3 (Strings) I explored how JavaScript handles text strings and the built-in properties and methods that make text manipulation effortless: 1. Template Literals (The Ultimate Game Changer) Shradha Didi introduced Template Literals , which use backticks ( ` ) instead of standard quotes. This allows us to perform String Interpolation —embedding variables directly inside a string using ${variable} . It makes code look clean and professional: javascript let obj = { item: "pen", price: 10 }; // Old way: console.log("The cost of", obj.item, "is", obj.price, "rupees."); // Modern way: console.log(`The cost of ${obj.item} is ${obj.price} rupees.`);

A few years ago, we started adapting our websites for mobile devices. Then we adapted them for...

AirDrop only works between Apple devices. Most alternatives require an app install, a cloud account, or route files through a third-party server. I wanted something simpler: Open a URL → discover nearby devices → send files. So I built LocalDrop — a peer-to-peer file transfer app that works entirely in the browser over local Wi-Fi. GitHub: https://github.com/akshaykdadheech/localdrop Live Demo: https://localdrop-4fddd39fb6ad.herokuapp.com How It Works Devices connected to the same Wi-Fi network automatically discover each other through a lightweight signaling server. Once discovered, WebRTC establishes a direct peer-to-peer connection: Browser A ──► Signaling Server ◄── Browser B └──────── WebRTC P2P ────────┘ The signaling server only helps devices find each other. File transfers happen directly between browsers via WebRTC and are DTLS encrypted, so the server never sees your files. Interesting Challenges Backpressure Handling WebRTC DataChannels on Chromium have a ~16 MB buffer limit. Sending data too aggressively can crash the tab. I solved this using: bufferedAmountLowThreshold Flow control based on drain events Cross-Platform Compatibility Different browsers expose different capabilities. Android Chrome supports the File System Access API iOS Safari does not This required separate file-receiving flows for each platform. Large File Transfers Keeping multi-gigabyte files in memory isn't practical. On Chrome, showSaveFilePicker() is triggered after the transfer completes, allowing transfer progress to remain visible throughout the process without buffering everything in RAM. Tech Stack Svelte 5 + Vite TypeScript WebRTC DataChannel Node.js + ws Docker Self-Hosting git clone https://github.com/akshaykdadheech/localdrop cd localdrop docker compose up -d Then open: http://your-ip:3001 from any device connected to the same Wi-Fi network. I'd love feedback from anyone who's worked with WebRTC DataChannels, especially on mobile browsers. If you find the project useful, a

When most people think about Java, they immediately picture enterprise applications, banking systems, massive backend services, or decades-old corporate software. While Java has earned its reputation in the enterprise world, that is only part of the story. Today, Java can run on devices as small as a Raspberry Pi, opening the door to hardware projects, edge computing, home automation, education, and hands-on learning experiences. Combining Java with Raspberry Pi creates a powerful platform for experimentation, learning, and building real-world solutions that go far beyond traditional enterprise development. Raspberry Pi teaches us about hardware. Java allows us to apply professional software engineering practices to that hardware. Together, they create a powerful platform for learning, prototyping, and building real-world IoT and edge computing solutions. Java Is More Than Enterprise Software Java's enterprise success has sometimes created the misconception that it only belongs in large organizations. In reality, modern Java offers: Excellent support for Linux and ARM architectures. High performance and low resource consumption. Modern frameworks such as Spring Boot, Quarkus, and Micronaut. Strong support for IoT and edge computing. Access to hardware through mature libraries. One of the largest developer ecosystems in the world. The Raspberry Pi highlights a different side of Java—one focused on creativity, experimentation, and direct interaction with the physical world. Instead of building another web application, you can build systems that sense, react, and interact with their environment. Java at the Edge One of the most exciting technology trends today is Edge Computing. Traditionally, devices send data to cloud services where processing and decision-making occur. Edge computing shifts part of that processing closer to where the data is generated. A Raspberry Pi running Java can: Process sensor data locally. Apply business rules before sending information to th

GitHub热门项目 | draw.io is a JavaScript, client-side editor for general diagramming. | Stars: 5,755 | 164 stars this week | 语言: JavaScript

GitHub热门项目 | Stealth headless browser for AI agents — bypass Cloudflare, bot detection, and anti-scraping. Drop-in Puppeteer/Playwright replacement. | Stars: 6,199 | 103 stars today | 语言: JavaScript

GitHub热门项目 | 🎬 seedance2接入 开源本地 AI 短剧 & 漫剧生成工具 —— 从故事到成片一站式完成，数据不出本机，短剧工作流管理平台，高灵活度，AI真人剧，AI漫剧本地搞定。 Open-source local AI short drama maker: story → storyboard → video, fully offline, your data stays yours. 纳米流水线 | Stars: 512 | 18 stars today | 语言: JavaScript

GitHub热门项目 | Unlimited FREE AI coding. Connect Claude Code, Codex, Cursor, Cline, Copilot, Antigravity to FREE Claude/GPT/Gemini via 40+ providers. Auto-fallback, RTK -40% tokens, never hit limits. | Stars: 15,880 | 287 stars today | 语言: JavaScript

GitHub热门项目 | ❤️ Fredy - [F]ind [R]eal [E]state [D]amn Eas[y] - Fredy keeps searching for new apartments, houses, and flats in Germany on platforms like ImmoScout24, Immowelt, Immonet, eBay Kleinanzeigen, and WG-Gesucht and instantly delivers the results to you via Slack, Telegram, Email, Discord or ntfy, so you can focus on the more important things in life ;) | Stars: 1,038 | 41 stars today | 语言: JavaScript

Keyboard accessibility is one of the most important — and most neglected — aspects of web accessibility. An estimated 2.5 million Americans have motor disabilities that prevent mouse use. If your site can't be operated entirely by keyboard, you're excluding them completely. The Four Core Principles WCAG 2.2 Principle 2 (Operable) contains the keyboard requirements: 2.1.1 Keyboard (AA): All functionality must be operable via keyboard 2.1.2 No Keyboard Trap (AA): If focus moves into a component, it must be possible to move it out 2.4.3 Focus Order (AA): If page can be navigated sequentially, order must be logical and predictable 2.4.7 Focus Visible (AA): Any keyboard-operable UI must have a visible focus indicator 2.4.11 Focus Appearance (AA, new in 2.2): Focus indicator must meet size and contrast requirements Testing Without Automated Tools Start with the basic keyboard test: Unplug (or ignore) your mouse Press Tab to move forward through interactive elements Press Shift+Tab to move backward Use Enter/Space to activate buttons, links, checkboxes Use arrow keys for radio groups, menus, sliders Use Escape to close dialogs and menus Any element you can't reach or activate? That's a WCAG 2.1.1 failure. The Most Common Keyboard Failures Custom dropdowns and menus // ❌ Keyboard inaccessible function Dropdown ({ items }) { return ( < div onClick = { toggle } className = "dropdown" > { items . map ( item => ( < div onClick = { () => select ( item ) } > { item . label } </ div > )) } </ div > ); } // ✅ Fully keyboard accessible function Dropdown ({ items }) { return ( < div role = "combobox" aria-haspopup = "listbox" aria-expanded = { isOpen } tabIndex = { 0 } onKeyDown = { handleKeyDown } // handles Enter, Space, Arrows, Escape className = "dropdown" > < ul role = "listbox" > { items . map (( item , i ) => ( < li key = { item . id } role = "option" tabIndex = { - 1 } aria-selected = { i === activeIndex } onKeyDown = { e => e . key === ' Enter ' && select ( item ) } > { item

The problem Every time I started a session with Claude Code I had to re-explain my entire project. What framework I use. How my folders are structured. What naming conventions I follow. What decisions I have already made. Every. Single. Session. It was slowing me down and I knew there had to be a better way. What I built I built stackbrief. One command scans your repo and opens a local visual dashboard showing your full codebase intelligence. npx stackbrief scan It opens a dashboard at localhost:3000 showing: Interactive code map of your architecture Dependency version comparison against npm Convention detection (naming, async patterns, error handling) Context health score MCP server so Claude Code pulls context automatically How it works stackbrief reads every file in your project and builds a structured understanding of it. It detects your framework, architecture pattern, modules, dependencies, and coding conventions. It then writes a CLAUDE.md file to your project and starts an MCP server on port 3001. Claude Code picks this up automatically before every session. No more explaining your project from scratch. AI chat that actually knows your code The dashboard has an Ask your codebase section. Unlike generic AI chat, this assistant has read every file in your project. Ask it about your own architecture and get answers specific to your code. Works with Ollama (free, fully local), Claude, OpenAI, or any OpenAI-compatible provider including Groq, Mistral, and local runners like LM Studio and AnythingLLM. Zero config, fully local No cloud. No telemetry. No account required. Everything runs on your machine. npx stackbrief scan That is it. The dashboard opens automatically. Try it GitHub: https://github.com/ragavtech/stackbrief Built with Node.js and TypeScript. Open source, MIT license. Would love to hear what you think.

A linked list is an ordered linear data structure where elements are not stored in sequential memory locations, instead they are stored in nodes that are linked together by a pointers. Linked list are used in data intensive application because linked list offer specific benefits for high frequency data manipulation, this benefits include: Efficient insertion and deletion Adding and removing elements from a linked list is highly efficient, unlike arrays which requires shifting all subsequent elements to maintain indexing. A linked list only requires updating the pointer. Dynamic sizing: linked list can grow or shrink during runtime without needing to pre-allocate memory. Memory management: Nodes in a linked list are only allocated when needed which prevents memory wastage. Flexible Traversal: Doubly and circular list allow you to move forward or backward, which makes them helpful for complex navigation The first node in a linked list is called the head which signifies the start of the list, while the last node is called the tail and has a pointer of null except in a circular linked list. Each node in a linked list has two things which are: the actual data the pointer or reference There are three main types of linked list: Singly linked list Doubly linked list Circular linked list Singly Linked List: Singly linked list are lists where each node has a next pointer that points to the next node. Doubly Linked List: Doubly linked list are list where each node has a next and previous pointer that points to the previous and next node. Circular Linked List: Circular linked list are list where the last node points back to the first node, forming a circle. Table of Contents create node class create linked list class isEmpty and getSize Methods prepend and append Method removeHead and removeTail Methods insert and search Methods getIndex and removeIndex Methods clear and print Methods create node class First let's open our code editor and create a new file called singlyLinkedLi

There's been no shortage of debate lately about whether grinding Leetcode still makes sense in the age of AI. I think it does. AI is a powerful tool, but it was built by humans; which means it inherited our strengths, our blind spots, and our biases. Leaning on it entirely without understanding what's happening under the hood is a risk. A mentor once told me: those who refuse to use AI are not hireable. But neither are those who rely on it entirely. Learning deeply is how you stay on the right side of that line. This is my journey into just that - learning deeply. Day 1 Leetcode 88: Merge Sorted Array This is an interesting problem. You begin with 4 pieces of data — 2 arrays and 2 integers: nums1 : a sorted array whose length equals nums1.length + nums2.length . The first m elements are valid numbers; the remaining indexes hold 0 s as placeholders. nums2 : a sorted array containing only valid numbers, with a length of n . m : the count of valid numbers in nums1. n : the count of valid numbers in nums2. The objective is to merge both arrays into sorted order in place . Since nums1 is already sized to hold every valid element from both arrays, it's where the final sorted result will live. Approach 1: Naive (Splice + Sort) This solution is 2 lines of code. That's it. It's a testament to how much ES6 advanced JavaScript. nums1 . splice ( m , n , ... nums2 ); nums1 . sort (( a , b ) => a - b ); Here's how it works. We start by calling .splice() on nums1. While .splice() has many use cases, here's what each argument is doing in this context: m : the index where we start deleting elements. Since m is the count of valid numbers in nums1, starting at index m puts us right at the first placeholder 0 — exactly where we want to be. n : the number of elements to delete. Since n equals the length of nums2, we're deleting exactly as many placeholders as we have values to insert. ...nums2 : the values we want to insert in place of the deleted elements. The ... is the spread operato

I recently built Letras Diferentes , a free Unicode text-styling tool for people who want creative copy-paste fonts for bios, nicknames, social posts and gaming profiles. The idea is simple: Type normal text Preview different Unicode styles Copy the result in one click Use it on Instagram, WhatsApp, TikTok, Free Fire, Discord or social posts The project is especially focused on Portuguese-speaking users, but the tool works with general Latin text too. Main project: One thing I’m learning while building it is that Unicode text tools are not just about “fonts”. They are about UX, compatibility, mobile performance, copy buttons, favorites, categories and making the result easy to use on real platforms. I’m still improving the interface, categories and mobile experience. Feedback is welcome.