标签：#Java

What I Built : ( https://github.com/Sai-Emani25/Solomon-s-Taskr ) I transformed my initial, bare-bones task management application, Taskr, into Solomon's Taskr—a significantly smarter, more robust productivity platform. The original project started as a standard way to log to-dos, but it lacked the intelligence to actually help manage time or prioritize effectively. With Solomon's Taskr, I wanted to build a system that doesn't just store data, but actively assists the user. Building this project means a lot to me because it represents a leap from writing basic applications to architecting intelligent, dynamic systems that solve real-world workflow bottlenecks. Demo Link to Final Repository: https://github.com/Sai-Emani25/Solomon-s-Taskr Link to Original Repository: https://github.com/Sai-Emani25/Taskr (Here is a quick walkthrough of Solomon's Taskr in action!) The Comeback Story The original Taskr project had been sitting in my repositories, unfinished and gathering dust. It was a classic case of starting a project with good intentions but abandoning it once the basic structure was complete. It could create, read, update, and delete tasks, but that was it. For the Finish-Up-A-Thon, I decided to completely resurrect and overhaul it. Here are the key changes and implementations that turned it into Solomon's Taskr: Complete Codebase Refactoring: I stripped down the old, inefficient logic and rebuilt the architecture to be highly scalable and maintainable. Intelligent Prioritization ("The Solomon Touch"): I integrated smart features to help organize and prioritize tasks rather than just listing them chronologically. (Note: If you integrated Gemini API or LLMs here for smart tagging, explicitly mention it!) Enhanced UI/UX: I moved away from the clunky, basic interface of the original Taskr and implemented a clean, responsive dashboard that provides a real-time overview of pending and completed tasks. Optimized Data Handling: I refined how the application processes and st

Why I stopped reading "❌ Old vs ✅ New" posts I used to scroll past them. Then I started ignoring them. Now? I don't read them at all. Not because they're "wrong". But because they're incomplete . The problem with "❌ Old vs ✅ New" These posts make everything look easy: One error One fix One clean "New Way" Three bullet points Save the post Done. Right? No. What these posts don't show you 🔹 The 200 failed deployments before that one working fix 🔹 The 300+ errors you solve along the way — not just one 🔹 The Vercel pipelines that break for no documented reason 🔹 The "New Way" that also fails in production 🔹 The gap between documentation and reality What happens in production That clean "New Way" code snippet? It might work on your local machine. But in production, with real traffic, real data, real edge cases? It can fail. Hard. And no three-line post prepares you for that. Why I stopped reading Because these posts teach me solutions to problems I don't have yet . But they don't teach me how to think when nothing works. They don't teach me: How to read error logs properly How to trace a pipeline failure across services How to stay consistent after multiple failed deploys How to know when the "New Way" is actually worse What actually helped me Not templates. Not shortcuts. Real experience: 200+ failed deployments 300+ errors solved (one by one) Broken pipelines fixed by understanding, not copy-paste Production live — not a "demo" or a "tutorial" This is not a "❌ vs ✅" post I'm not giving you a "Here's the fix". Because the real fix isn't three lines of code. It's patience. It's persistence. It's failing and getting back up. And no post can save that to your bookmarks. 👇 Have you ever followed a "New Way" post and had it fail in production?

Static Application Security Testing (SAST) is a critical practice in modern DevSecOps. While tools like SonarQube, Snyk, and Veracode are popular, this article focuses on GitHub CodeQL – a semantic code analysis engine that treats code as a database. We will apply it to a vulnerable Java Spring Boot application to detect SQL Injection and Path Traversal. 🤔 Why CodeQL? Unlike pattern-based scanners, CodeQL builds a relational database of your code, including abstract syntax trees, control flow graphs, and data flow graphs. This allows it to track tainted data across functions, classes, and files, drastically reducing false positives. 🚨 Target Application (Vulnerable Java App) Let's look at a simple REST API with two vulnerable endpoints. File: UserController.java package com.demo.controller ; import com.demo.model.User ; import org.springframework.beans.factory.annotation.Autowired ; import org.springframework.jdbc.core.JdbcTemplate ; import org.springframework.web.bind.annotation.* ; import java.nio.file.Files ; import java.nio.file.Paths ; import java.util.List ; @RestController @RequestMapping ( "/api" ) public class UserController { @Autowired private JdbcTemplate jdbcTemplate ; // Vulnerability 1: SQL Injection @GetMapping ( "/users" ) public List < User > getUsers ( @RequestParam ( "id" ) String userId ) { String sql = "SELECT * FROM users WHERE id = " + userId ; // Dangerous concatenation return jdbcTemplate . query ( sql , ( rs , rowNum ) -> new User ( rs . getString ( "id" ), rs . getString ( "name" ))); } // Vulnerability 2: Path Traversal @GetMapping ( "/file" ) public String readFile ( @RequestParam ( "filename" ) String filename ) throws Exception { return new String ( Files . readAllBytes ( Paths . get ( "/var/data/" + filename ))); } } 🛠️ Installing and Configuring CodeQL CLI You can run CodeQL locally to analyze your code before pushing it to a repository. 1. Download CodeQL from GitHub releases: wget [ https://github.com/github/codeql-cli-binaries/re

GitHub热门项目 | 🤖一个基于 WeChaty 结合 ChatGPT / Claude / Kimi / DeepSeek / Ollama等Ai服务实现的微信机器人 ，可以用来帮助你自动回复微信消息，或者社群分析/好友管理，检测僵尸粉等... | Stars: 10,760 | 128 stars this week | 语言: JavaScript

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GitHub热门项目 | Digitalization of Judiciary System | Stars: 86 | 4 stars today | 语言: JavaScript

GitHub热门项目 | Community curated list of templates for the nuclei engine to find security vulnerabilities. | Stars: 12,471 | 9 stars today | 语言: JavaScript

GitHub热门项目 | Grab your own sweet-looking '.is-a.dev' subdomain. | Stars: 10,416 | 7 stars today | 语言: JavaScript

GitHub热门项目 | The Block Editor project for WordPress and beyond. Plugin is available from the official repository. | Stars: 11,682 | 3 stars today | 语言: JavaScript

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If you've ever built an Electron app that needs a backend, you know the problem. You want something lightweight, something that runs on Node, something where you don't spend three hours configuring Swagger, and something that doesn't make you feel like you're setting up a microservices architecture just to expose two endpoints to your own desktop UI. I've shipped Electron apps with Express. I've tried Fastify. Both work, but they leave you doing a lot of plumbing yourself. Then I found DaloyJS , and honestly, it clicked. The Electron Backend Problem Here's the thing about Electron: your renderer process is basically a browser, and your main process is a Node.js server. When you need data from external APIs, or you need a clean layer between your UI and your business logic, you want a BFF, a Backend for Frontend. A thin server that composes upstream calls, holds the session, and returns exactly the shape your UI needs. DaloyJS was built for this role. The docs even say so plainly: typed upstream client, fetchGuard for safe egress, session handling, and edge runtime support. That combination is exactly the BFF toolkit. Contract-First Means Less Glue The killer feature for desktop apps is contract-first routing. You define a route once, and DaloyJS gives you validation, OpenAPI 3.1 docs, and a typed in-process client all from the same source. No stale spec files. No writing types by hand. Here's what a basic Electron BFF route looks like: import { z } from " zod " ; import { App , requestId , secureHeaders , rateLimit } from " @daloyjs/core " ; import { serve } from " @daloyjs/core/node " ; const app = new App ({ bodyLimitBytes : 1 << 20 , requestTimeoutMs : 5 _000 , docs : true , // auto-mounts /docs and /openapi.json }); app . use ( requestId ()); app . use ( secureHeaders ()); app . use ( rateLimit ({ windowMs : 60 _000 , max : 120 })); app . route ({ method : " GET " , path : " /settings/:userId " , operationId : " getUserSettings " , request : { params : z . objec

The test hung for 30 seconds. The response had already fired. One moved line fixed it. The test hung for 30 seconds, then timed out. The browser had received the response. The page had loaded. The data was there. The test was still waiting. The wizard I was writing a helper to walk through a 4-step booking wizard. After clicking "Next" on step 1, the page does a full navigation — window.location.href to step 2. Step 2 immediately loads doctor data from the API. The helper looked like this: await Promise . all ([ page . waitForURL ( /step=2/ ), step1Next . click ()]); await page . waitForResponse ( r => r . url (). includes ( ' /doctors ' )); Standard pattern: wait for navigation, then wait for the data request. Timeout. Every time. What I checked first The URL pattern. Maybe /doctors wasn't matching. Opened the network tab. The request was there: GET /api/v1/doctors , 200, 47ms. Correct URL, correct response. The page looked fine. The data was rendered. The test said it was waiting for a response that had already happened. Added waitForLoadState . Still hung. Added an explicit waitForSelector for an element that was clearly on the page. That passed. Then waitForResponse hung again. The response existed. The test couldn't see it. What was actually happening page.waitForResponse() is not a query. It doesn't look at what happened. It registers a listener — from that exact moment forward — and waits for the next matching response. The sequence in my code: Promise.all resolves when the URL changes to step=2 By the time the URL changed, step 2 had already loaded Step 2 had already sent and received /api/v1/doctors Then waitForResponse registered its listener Now it's waiting for the next /doctors response Which never comes Playwright doesn't buffer missed events. If the response fired before the listener was registered — it's gone. The fix await Promise . all ([ page . waitForURL ( /step=2/ ), page . waitForResponse ( r => r . url (). includes ( ' /doctors ' )), step1Next

Built a full-stack RBAC admin starter: NestJS (Clean Architecture) + Next.js 16 (FSD). JWT refresh, permission-gated UI, sheet-based CRUD. MIT. Looking for feedback. ⚡ Next.js 16 Admin Dashboard Template Architecture: Strictly adheres to Feature-Sliced Design (FSD) to prevent codebase rot in large applications. Key Features: Full-scale Role-Based Access Control (RBAC) UI, URL-driven advanced tables (TanStack Table v8), global caching (TanStack Query v5), and dynamic sheet-based UX configurations using shadcn/ui and Tailwind v4. Quality Assurance: Pre-configured with Playwright for End-to-End (E2E) testing and automated GitHub Actions CI. 🛡️ NestJS Clean Architecture REST API Architecture: Implements strict layered Clean Architecture (Presentation ➔ Application ➔ Domain 🡨 Infrastructure) ensuring zero database/framework lock-in. Key Features: Advanced authentication via JWT refresh rotation, stateful RBAC with high-performance Redis permissions caching, and enterprise-grade security structures. Quality Assurance: Achieves ~98% test coverage across domain and application layers using Jest.

TypeORM 1.0 is the first major release of the open-source TypeScript and JavaScript ORM since its inception in 2016. This version modernizes platform requirements, removes deprecated APIs, and introduces numerous bug fixes and new features. TypeORM now supports ECMAScript 2023, dropping older Node.js versions and dependencies while enhancing security and migration processes. By Daniel Curtis

Stop Leaking Trace Context: How to Migrate OpenTelemetry to JDK 26 Scoped Values If you are still relying on traditional ThreadLocal storage for OpenTelemetry context propagation under JDK 26's virtual threads, you are sitting on a production time bomb. Millions of concurrent virtual threads will quickly turn your heap into a graveyard of leaked trace contexts and bloated memory overhead. If you're prepping for interviews, I've been building javalld.com — real machine coding problems with full execution traces. Why Most Developers Get This Wrong Defaulting to ThreadLocal: Assuming the default OpenTelemetry ThreadLocal storage works fine with virtual threads, ignoring the heavy heap footprint and context drift when threads are unmounted and rescheduled. Ignoring Context Leakage: Forgetting that ThreadLocal values persist unless explicitly removed, causing trace data to bleed into unrelated tasks on shared carrier threads. Manual Propagation Mess: Manually passing Span objects down the call stack instead of leveraging JDK 26's native scoped value propagation. The Right Way The clean solution is to bind OpenTelemetry's ContextStorage directly to JEP 487 Scoped Values to enforce immutable, automatic, and thread-safe context propagation across virtual threads and structured concurrency boundaries. Implement Custom ContextStorage: Create an OTel ContextStorage implementation backed by a static ScopedValue<Context> . Enforce Immutability: Leverage the immutable nature of ScopedValue to prevent downstream child threads from accidentally mutating the parent's tracing context. Leverage Structured Concurrency: Use StructuredTaskScope which automatically inherits the scoped trace context without manual boilerplate. Show Me The Code Here is how to run a span using JDK 26 ScopedValue for zero-leak, zero-overhead propagation: public class ScopedTraceRunner { private static final ScopedValue < Span > ACTIVE_SPAN = ScopedValue . newInstance (); public void execute ( Span span , Runn

GitHub热门项目 | The GEP-powered self-evolving engine for AI agents. Auditable evolution with Genes, Capsules, and Events. | evomap.ai | Stars: 7,871 | 140 stars today | 语言: JavaScript

Web Security: OWASP Top 10 and How to Fix Them (2026) Security isn't a feature you add later — it's built into every layer. Here's how the top 10 vulnerabilities work and how to prevent them. #1 Broken Access Control // ❌ Vulnerable: User can access anyone's data app . get ( ' /api/users/:id ' , ( req , res ) => { const user = await db . users . findById ( req . params . id ); res . json ( user ); // No check if requester owns this data! }); // ✅ Secure: Always verify ownership app . get ( ' /api/users/:id ' , async ( req , res ) => { // Check: Is the logged-in user requesting their OWN data? if ( req . params . id !== req . user . id && req . user . role !== ' admin ' ) { return res . status ( 403 ). json ({ error : ' Access denied ' }); } const user = await db . users . findById ( req . params . id ); res . json ( user ); }); // ✅ Better: Use middleware for all protected routes const requireOwnership = ( resourceType ) => async ( req , res , next ) => { const resource = await db [ resourceType ]. findById ( req . params . id ); if ( ! resource ) return res . status ( 404 ). json ({ error : ' Not found ' }); if ( resource . userId !== req . user . id && req . user . role !== ' admin ' ) { return res . status ( 403 ). json ({ error : ' Access denied ' }); } req . resource = resource ; // Attach for route handler next (); }; app . get ( ' /api/posts/:id ' , auth , requireOwnership ( ' posts ' ), ( req , res ) => { res . json ( req . resource ); }); #2 Cryptographic Failures // ❌ Storing passwords in plain text or weak hashing const password = " password123 " ; db . users . insert ({ email , password }); // NEVER DO THIS! // ✅ Proper password hashing with bcrypt const bcrypt = require ( ' bcrypt ' ); const SALT_ROUNDS = 12 ; // Higher = slower = more secure (12 is good balance) async function hashPassword ( password ) { return bcrypt . hash ( password , SALT_ROUNDS ); } async function comparePassword ( password , hash ) { return bcrypt . compare ( password , hash ); /

Web Security: OWASP Top 10 and How to Fix Them (2026) Security isn't a feature you add later — it's built into every layer. Here's how the top 10 vulnerabilities work and how to prevent them. #1 Broken Access Control // ❌ Vulnerable: User can access anyone's data app . get ( ' /api/users/:id ' , ( req , res ) => { const user = await db . users . findById ( req . params . id ); res . json ( user ); // No check if requester owns this data! }); // ✅ Secure: Always verify ownership app . get ( ' /api/users/:id ' , async ( req , res ) => { // Check: Is the logged-in user requesting their OWN data? if ( req . params . id !== req . user . id && req . user . role !== ' admin ' ) { return res . status ( 403 ). json ({ error : ' Access denied ' }); } const user = await db . users . findById ( req . params . id ); res . json ( user ); }); // ✅ Better: Use middleware for all protected routes const requireOwnership = ( resourceType ) => async ( req , res , next ) => { const resource = await db [ resourceType ]. findById ( req . params . id ); if ( ! resource ) return res . status ( 404 ). json ({ error : ' Not found ' }); if ( resource . userId !== req . user . id && req . user . role !== ' admin ' ) { return res . status ( 403 ). json ({ error : ' Access denied ' }); } req . resource = resource ; // Attach for route handler next (); }; app . get ( ' /api/posts/:id ' , auth , requireOwnership ( ' posts ' ), ( req , res ) => { res . json ( req . resource ); }); #2 Cryptographic Failures // ❌ Storing passwords in plain text or weak hashing const password = " password123 " ; db . users . insert ({ email , password }); // NEVER DO THIS! // ✅ Proper password hashing with bcrypt const bcrypt = require ( ' bcrypt ' ); const SALT_ROUNDS = 12 ; // Higher = slower = more secure (12 is good balance) async function hashPassword ( password ) { return bcrypt . hash ( password , SALT_ROUNDS ); } async function comparePassword ( password , hash ) { return bcrypt . compare ( password , hash ); /

Rust Ownership System Explained for JavaScript Developers Quick context (why you're writing this) I was trying to rewrite a small utility I’d written in JavaScript—a function that takes a string, splits it into words, and returns the longest one. In JS it’s trivial: you pass the string around, mutate arrays, and nothing blows up. When I attempted the same thing in Rust, the compiler kept yelling at me about “use of moved value” and “cannot borrow as mutable because it is also borrowed as immutable”. I spent a good chunk of an afternoon staring at those errors, thinking I’d missed some syntax detail, only to realize the real issue was a completely different way of thinking about data. If you’ve ever felt that Rust’s compiler is being overly pedantic, you’re not alone—but once you grasp what it’s protecting you from, the frustration turns into appreciation. The Insight Rust doesn’t treat variables like JavaScript’s loosely‑typed references. Instead, it enforces ownership at compile time. Three ideas tend to surprise developers coming from a garbage‑collected world: Move semantics – assigning a value to another variable moves it; the original is no longer usable unless you explicitly clone it. Borrowing rules – you can have either many immutable references or exactly one mutable reference to a piece of data, but never both at the same time. Lifetimes – the compiler tracks how long references are valid, preventing dangling pointers without a garbage collector. The first two are the ones that trip people up most often, and they directly address the class of bugs JavaScript developers know all too well: accidental shared‑state mutations and use‑after‑free‑like mistakes (though in JS they show up as weird undefined values rather than crashes). Let’s look at each with a concrete example, show the common mistake, and then see how to do it right. How (with code) Move semantics – the “you can’t use it after you give it away” surprise fn main () { let greeting = String :: from

We run an app where you describe a task and an AI agent does it. The first step after you hit submit is a planning call: POST /api/web/tasks/plan, which turns your free text into a structured plan the agents can pick up. One submit should mean one plan. While testing locally I noticed two plan requests going out per submit. Same payload, fired back to back. The agents handled it fine because the second plan just overwrote the first, but it bothered me. A doubled write is a doubled write, and the next one might not be idempotent. First wrong guess: a double-click My first assumption was the obvious one. The user double-clicks, or the button is not disabled during the request, so two clicks sneak through. I added the disabled state, watched the network tab, and got two requests from a single click. So it was not the button. The thing I had stopped seeing The submit logic lived in an effect. When the form phase flipped to submitting, the effect ran and fired the plan call. There was a second effect too: when the user changed the tier or output format mid-flow, a matching effect re-planned, because a different tier means a different plan. Neither effect had any guard against running twice. And in development, React StrictMode mounts every component, unmounts it, and mounts it again, on purpose, to surface effects that are not safe to re-run. My plan effect was exactly the kind of effect StrictMode is built to expose. The double mount fired it twice. The detail that made it click: I built the app for production and watched the network tab there. Exactly one request. The double was a development-only artifact of StrictMode doing its job. The bug was never in production traffic, but the fact that StrictMode could double it meant my effect was not safe, and an unsafe effect is a latent bug waiting for a real remount. The fix: ref guards set before the await, not reset in cleanup The instinct is to reach for a boolean. The catch is where you reset it. If you reset the guard