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As developers, we often lose hours to repetitive tasks - formatting data, debugging auth tokens, or wrestling with encoding issues. The right set of utility tools can cut that overhead significantly. Here is a curated breakdown of the categories and tools worth keeping in your daily workflow in 2026. JSON and Data Formatting Tools Working with raw, minified API responses is a real productivity killer. A browser-based JSON formatter with real-time validation and proper indentation helps you parse and debug responses in seconds - without sending your data to third-party servers. Similarly, XML is far from dead; SOAP APIs and enterprise integrations still rely on it, so a solid XML formatter with XPath support is worth having around. On the frontend, a CSS minifier that handles dead code removal and selector optimization can meaningfully reduce your page load times. Data Conversion Utilities Modern stacks often have to bridge the gap between formats. A JSON-to-XML converter is critical when integrating with legacy systems, but look for one that handles arrays and special characters correctly. CSV-to-JSON converters help structure flat export data from spreadsheets or databases into something your application can consume. And if your deployment pipeline involves YAML config files, a formatter that catches indentation errors before they break your CI/CD run is essential. Encoding and Security Helpers Base64 encoding comes up constantly - from embedding images in CSS to building HTTP Basic Auth headers. A reliable encoder/decoder handles all these cases without fuss. URL encoding matters too; unencoded special characters in query strings cause subtle API bugs that are surprisingly hard to track down. For authentication debugging, a JWT decoder lets you inspect token payloads without needing to verify the signature - ideal for local development and troubleshooting. Code Generation and Color Utilities Distributed systems need collision-resistant unique IDs - a UUID generato

I'm a 17-year-old IT student from Luxembourg. A few months ago I got tired of spending 2-3 hours a day manually browsing Upwork, Malt, and Freelancer looking for projects. So I built an automation system that does it for me — 24/7, on a Raspberry Pi 3. Here's what it does, how I built it, and every painful lesson I learned along the way. What the system does Scans Upwork, Malt, and Freelancer every 30 minutes Scores each job 0–100 with AI based on my profile Generates proposals in English, French, and German Sends the best jobs to Telegram with inline A/B buttons Tracks which proposal style gets more replies Sends daily stats and weekly market trend reports Reminds me to follow up after 3 days The stack n8n — self-hosted workflow automation (Docker on Raspberry Pi 3) Groq API (Llama 3.1-8b-instant) — AI scoring and proposal generation Supabase — PostgreSQL database for jobs, proposals, clients SerpAPI — searching job boards via Google Apify — scraping Upwork listings Telegram Bot API — alerts and bot commands Cloudflare Tunnel — HTTPS for webhooks Total running cost: ~$5/month. 7 workflows 01 - Job Discovery — runs every 30 minutes, searches 10+ sources, deduplicates via Supabase unique constraint on URL 02 - Proposal Generator — AI scores the job, generates two proposal variants (formal vs hook-first), sends to Telegram with A/B buttons 03 - Follow-up Reminders — checks Supabase every 3 days for unanswered proposals 04 - CRM via Telegram — full client management through bot commands (/jobs, /stats, /clients) 05 - Market Intelligence — daily report: how many jobs found, average score, top platforms 06 - Trend Analysis — weekly report on what skills are trending in automation 07 - Lead Generation — finds companies actively using Zapier or Make who might want to switch to n8n Lessons learned (the hard way) 1. Cyrillic text breaks JSON body nodes silently If you have Cyrillic characters in a JSON body field with newlines, n8n throws a "Bad control character" error. Kee

I got tired of paying for form services or spinning up a backend just to handle contact form submissions. So I built RG Forms — a contact form endpoint backed entirely by a Google Sheet you own. No server, no monthly fee, no third-party storing your data. The idea Most form tools store your submissions on their servers. You pay monthly, you depend on their uptime, and your data lives in their database. RG Forms does the opposite: every submission goes straight into a Google Sheet in your own Google Drive, sent by an Apps Script that you own and control. RG Forms provisions that sheet and script for you in about 90 seconds. After that, your endpoint runs forever at no cost — completely independent of any RG Forms server. Built for static sites If you host on GitHub Pages, Netlify, Vercel, Cloudflare Pages, or just plain HTML on a CDN, you've hit this wall: there's no backend to receive a form POST. The usual workarounds are a paid form service, a serverless function you have to write and maintain, or standing up a whole backend just for a contact form. RG Forms is built exactly for this. Your endpoint is a plain HTTPS URL you POST to straight from client-side JavaScript — no build step, no serverless function, no server of any kind. Drop the fetch call into your page and you're done. It pairs naturally with any static-site generator (Hugo, Jekyll, Astro, Eleventy, Next export) and any no-code builder that lets you add a snippet of JS. Your static site stays static; the form just works. How it's built RG Forms is a fully static web app. There's no RG Forms server, no database, no backend. Every API call during setup goes directly from your browser to Google using your own OAuth token. Setup (one time, in your browser): Your Browser ├─── Google OAuth ──▶ Short-lived token (memory only) ├─── Google Drive API ──▶ Creates Sheet + Drive folder └─── Apps Script API ──▶ Creates & deploys form handler Live endpoint (after provisioning): Your Website / App └─── POST to script

In my last post I complained — a lot — about product managers and how they made my life hell with vibe code. PS: apologies, manager, if you're reading this — but it's true. Now, I'm not here just to complain. There were a lot of learning opportunities too, like how to handle legacy / vibe code. Because at the end of the day, both are the same: no one knows how they work, but somehow they keep working. Touching them is like defusing a bomb — you never know how your change might cascade and break the core logic. The good news is that vibe code is much simpler than legacy. AI, in all its glory, tries to write perfect-looking code — proper function names, comments, the works — not like legacy code where a single function runs 500 lines, with spaghetti names all over that make no sense and comments that are out of date. And that makes it something I can actually handle. I still don't have a perfect, step-by-step playbook — but I've got pieces. The first one. The cheapest and the oldest one. The one the industry solved decades ago and the whole "AI built my app in a day" crowd somehow forgot exists. A linter. Yes, you heard me right. A linter. ESLint. Most people who've been in this industry already know it. It's the most boring, reliable tool in the box. But in an era where the answer to every problem is "add another AI," it's worth saying out loud why the boring tool still wins. What a linter actually is If you vibe-coded your way into this world, or you're new to web dev in general and have never heard the word "lint", here's the honest version. A linter is a set of rules you add to your repo. It reads your code without running it, checks it against those rules, and flags everything that's broken, sloppy, or about to bite you in production. The detail people get wrong: it's not a grep for bad words. A real linter parses your code into a syntax tree and actually reasons about its structure — what's imported, what's called, what's reachable, what types flow where. That's

GitHub热门项目 | Concise, consistent, and legible badges in SVG and raster format | Stars: 26,767 | 55 stars this week | 语言: JavaScript

GitHub热门项目 | Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience | Stars: 61,365 | 71 stars today | 语言: JavaScript

Building a "paste a YouTube link, get a transcript" feature sounds trivial until you deploy it to a server. The moment your request comes from a datacenter IP instead of a residential one, YouTube responds with LOGIN_REQUIRED or quietly serves nothing. Here's how VidTranscriber handles it. The problem There are two ways to get text from a YouTube video: Existing captions — if the uploader (or YouTube's auto-caption) provides them, you can fetch the caption track directly. Fast, free, no transcription needed. Transcribe the audio — pull the audio stream and run it through a speech-to-text model (Whisper-family). Works for any video, but costs compute. Both start with talking to YouTube from your server — and that's where it breaks. YouTube aggressively gates datacenter traffic: the watch page and InnerTube API return LOGIN_REQUIRED , and naive audio fetching gets reCAPTCHA'd. The approach The fix is to separate where the request originates from where the work happens : A Cloudflare Worker handles the user request and orchestration. Caption/audio fetching is routed through a path whose egress isn't treated as a bot — so the LOGIN_REQUIRED wall doesn't trigger. Captions, when available, become the primary path (no transcription cost). Only when there are no usable captions do we fall back to downloading audio and running Whisper. Long jobs go onto a queue (Cloudflare Queues) so the request returns immediately and the transcript streams in as it completes. Why captions-first matters Most "transcript generator" traffic is for videos that already have captions — talks, tutorials, news. Serving those from the caption track is instant and free, which means the expensive Whisper path is reserved for the minority of videos that actually need it. That's the difference between a tool that's cheap to run and one that isn't. What's still hard IP reputation drifts — what works today can get throttled tomorrow, so the extraction path needs monitoring and fallbacks. Caption quality

I have watched founders lose sales calls they should have won. Not because they lacked skill. Not because the offer was wrong. Because they walked in to prove they were smart — instead of finding out whether the pain was real. Sales Is Diagnosis Plus Decision The call is not there for you to pitch. The call is there to find out: Is the pain real? Does the buyer have urgency? Does the budget exist? Can a fixed-scope sprint create a clear win? That is it. Four questions. Everything else follows from those. Sales is not pressure. Sales is diagnosis plus decision. 1️⃣ The Call Structure That Works Frame the call in the first 60 seconds: "I'll understand the current state, ask what is costing you, then tell you whether a sprint makes sense. If it doesn't, I'll say so." That sentence does 3 things: Sets expectations — no pressure, no hard close Signals competence — you have done this before Removes the buyer's guard — they can be honest about what is broken Then run this flow: 1️⃣ Current state — what exists now? 2️⃣ Pain — what is broken or slow? 3️⃣ Cost — what does it cost in time, money, trust, or delay? 4️⃣ Urgency — why now? 5️⃣ Decision — who approves? 6️⃣ Success — what would make this worth paying for? 7️⃣ Close — recommend the sprint or walk away 2️⃣ The Questions That Reveal Money These are the 6 questions I use to find whether a sprint is worth recommending: "What happens if this stays broken for another 30 days?" — reveals urgency and cost "What have you already tried?" — reveals how serious they are "Where does the current process lose leads, users, time, or trust?" — reveals the money leak "Who feels this pain most inside the business?" — reveals whether the buyer is also the decision-maker "What would make this an obvious win?" — reveals success criteria before you price "If we fixed only one thing first, what would matter most?" — reveals scope Listen for the answer with the money in it. That is the thing you fix. That is what you price. That is the sprin

The conversation I keep having with AI founders goes like this: "I've sent 50 DMs. No one is biting." Then I look at the offer. "I build AI automations for businesses." There is the problem. Bad, Better, Best — The Offer Anatomy Breakdown Most technical people sell their skill. Buyers do not buy skill. They buy a removed headache. Let me break down the bad/better/best framework I use for every offer I build: Bad: I build AI automations for businesses. Better: I help service businesses automate lead follow-up so no enquiry gets ignored. Best: I install a 7-day lead recovery system that captures, qualifies, follows up, and tracks every new enquiry — so missed leads stop disappearing into WhatsApp, email, and memory. The best version does 4 things in one sentence: Names the buyer — service businesses Names the painful outcome — missed leads disappearing Names the mechanism — a 7-day lead recovery system Names the specific result — follows up, tracks, captures That is not wordsmithing. That is the difference between getting ignored and starting a conversation. 1️⃣ The Eight-Part Offer Anatomy Every offer worth selling should answer all 8 of these: Part What It Does Buyer Who exactly has this pain? Pain What expensive thing is broken? Outcome What changes after the sprint? Mechanism What system creates the outcome? Timeline How quickly does the buyer see progress? Deliverables What exactly is included? Proof Why should the buyer believe it? CTA What is the next small step? If any row in that table is blank for your current offer — you are leaving money in the explanation gap. 2️⃣ The Offers That Actually Sell Here is what the strongest AI service offers look like right now. Not vague consulting. Fixed-scope sprints with outcomes. "I turn your AI-built app from fragile demo into launch-ready product" — auth, payments, logging, analytics, deployment, launch-readiness report — in 7–14 days. Price: $2,500–$7,500. "I build your founder-led GTM system" — content engine, lead c

TLDR tsc is the TypeScript compiler. It turns your .ts files into .js files that Node.js and browsers can run. Install it with npm install -g typescript . Run tsc to compile your whole project. Run tsc --watch to auto-compile on every file save. Run tsc --noEmit to check for errors without creating any files. What is the TypeScript Compiler? The TypeScript compiler is a tool called tsc . It reads your TypeScript files and turns them into JavaScript files. Your browser and Node.js cannot run TypeScript directly. They only understand JavaScript. So tsc acts as the bridge between what you write and what actually runs. Think of tsc like a spell checker for your code. It finds problems before your code ever runs. Then it produces clean JavaScript output for you. How to Install the TypeScript Compiler You install tsc using npm. There are two ways to do this. Option 1: Global Install (runs anywhere on your computer) npm install -g typescript After install, check it works: tsc --version You will see something like Version 6.0.3 . Option 2: Local Install (recommended for teams) npm install --save-dev typescript Then run it using npx : npx tsc --version Which one should you use? Use a local install for project work. This makes sure everyone on your team uses the same TypeScript version. Use a global install only for quick personal experiments. How to Compile a Single TypeScript File The simplest way to use tsc is to pass it a single file. Create a file called hello.ts : const message : string = " Hello, TypeScript! " ; console . log ( message ); Now compile it: tsc hello.ts This creates a new file called hello.js in the same folder: var message = " Hello, TypeScript! " ; console . log ( message ); Notice that TypeScript removed the : string type annotation. The output is plain JavaScript that Node.js can run. Run the output file: node hello.js # Hello, TypeScript! Important: When you pass a file directly to tsc , it ignores your tsconfig.json . It uses its own default setting

Last week I shipped something that would have sounded reckless two years ago: an MCP server that lets an AI agent write to a merchant's live Shopify store. Create discount codes. Build customer segments. Draft WhatsApp campaigns against real order data. Read-only agent integrations are everywhere now, and they're fine — but a read-only agent is just a chatbot wearing a dashboard. The useful version is the one that does the thing . The dangerous version is also the one that does the thing. A hallucinated SELECT is a wrong answer; a hallucinated discount code is free product going out the door. So before turning writes on, I sat down and listed every way an agent could hurt a store. Then I built one guardrail per failure mode. Here's the list — it's short, and I think it generalizes to any agent surface that touches business data. 1. Read-only by default. Writes are a per-token opt-in. The lazy design is one API key that can do everything the app can do. Instead, every agent token starts read-only — list customers, inspect segments, read campaign stats. Write capability is a separate flag the merchant flips per token: { "token" : "fav_mcp_…" , "scopes" : [ "read" ], // default "writeEnabled" : false // explicit opt-in , per token } This sounds obvious. It is obvious. It's also the single most-skipped step I see in agent integrations, because it's friction during development. Build the friction in anyway — "the agent could read everything but couldn't have sent that" is a sentence you really want available to you later. 2. Caps live in the tool schema, not the prompt. Early on I had a system prompt that said something like "never create discounts above 30%." You can guess how durable that is. Prompts are suggestions; schemas are physics. So the caps moved into the tool's input validation itself — the shape of it: // create_discount — validated server-side, not prompt-side { percentage : z . number (). min ( 1 ). max ( 100 ), // hard ceiling, enforced in the service exp

A few months ago I was doing a final pre-release review on a client project before handing it...

Building a URL shortener with Cursor, ChatGPT, AWS Lambda, API Gateway, and Cloudflare taught me more about shipping software than writing code. Since last year, Cursor has been my go-to development assistant for daily tasks. But at the beginning of this year, just using it to generate snippets didn't feel like enough anymore. Having studied programming since 2011, I knew what modern AI tools could do, but I wanted to test their limits. I decided to build a full project—a URL shortener—without writing a single line of frontend or backend code myself. For the stack, I chose Node.js with TypeScript, React with Vite, and AWS Lambda to handle redirects. While I used ChatGPT to debate architectural trade-offs, Cursor generated the entire codebase. Watching a functional application take shape so quickly was the exact moment a line was crossed for me. It made me realize how fundamentally software development is shifting: our role is evolving from code writers to architectural decision-makers and problem-solvers. But truth be told, this project and this post represent a massive personal milestone. Like many developers, I have a graveyard of half-finished projects on my machine. This is one of the first times I've pushed a personal project all the way to production. Writing this and exposing my work to the community is a huge first step for me. This isn't just a story about code or AI—it's about the challenge of finally shipping something. The Stack Before asking Cursor to write a single line of code, I wanted to map out the architecture. I didn't need a massive enterprise system for a URL shortener, but I did want something flexible enough to support future features and experiments. To validate my ideas, I used ChatGPT to debate the pros and cons of different architectural approaches. We eventually settled on this stack: Backend: Node.js + TypeScript Frontend: React + Vite Database: MongoDB Atlas Redirects: AWS Lambda Entry Point: AWS API Gateway DNS / CDN: Cloudflare Secur

GitHub热门项目 | The library for web and native user interfaces. | Stars: 245,699 | 547 stars this week | 语言: JavaScript

Exactly one year ago, my terminal was a blank slate. I started where almost everyone does , wrestling with HTML, CSS, and JavaScript, trying to understand the web pixel by pixel. What began as curiosity quickly turned into a full obsession. I went from building simple static pages to diving deep into full-stack development. Here’s what that intense first year of constant building, breaking things, and shipping real projects has looked like. The Leap into Modern Frameworks Once vanilla JavaScript started feeling limiting, I jumped into React . Component-based thinking completely changed how I approached interfaces. Then came Next.js — it bridged client-side beauty with server-side power and pushed me into a true full-stack mindset. The Ultimate Test: Lynvista Safaris The biggest challenge was building lynvistasafaris.com , a live travel booking platform for a real client. This project forced me far beyond tutorials and into real engineering problems. I had to implement: Payment infrastructure from scratch with Daraja API (M-Pesa STK pushes) and Paystack for international transactions. A robust database layer using Drizzle ORM + MySQL. Custom business logic , including a multi-currency pricing system with special validation rules for currencies like GBP. It was messy , many late nights debugging webhooks, schema mismatches, and edge cases , but shipping it taught me more than any course ever could. What One Year Taught Me Syntax is just a tool. The real skill is learning how to break down complex problems, debug effectively, and keep iterating even when things break in production. I’m incredibly proud of the progress I’ve made in 365 days (402 contributions later), but I know I’m still at the very beginning. For the next phase, I’m focused on shipping more projects, writing about the crazy bugs I encounter, and deepening my architectural and full-stack skills. If you want to see what I’m building right now, check out my GitHub .

Why I built this Every time I started a new Java full stack project I was spending 2-3 days just on setup — JWT configuration, Spring Security, CORS, connecting Angular to backend. So I decided to build a reusable starter kit once and never do that setup again. What I built A complete full stack starter kit with: Spring Boot 3.5 REST API Angular 19 frontend connected to backend MySQL database with User table ready JWT Authentication working out of the box Spring Security configured Full CRUD operations Clean layered architecture (Controller → Service → Repository) The Tech Stack Backend: Java 17, Spring Boot, Spring Security, JWT, JPA Frontend: Angular 19, TypeScript Database: MySQL How it works User registers via POST /api/users User logs in via POST /api/auth/login Backend returns JWT token Frontend stores token in localStorage All protected routes require valid token Invalid or missing token returns 401 Unauthorized What I learned JWT configuration in Spring Security is confusing at first CORS needs to be configured in SecurityConfig not just main class Angular HttpClient needs provideHttpClient() in app.config.ts Service layer keeps code clean and testable GitHub Full source code is available here: https://github.com/shindebuilds/springboot-angular-starter-kit Feel free to clone it, use it, improve it. If you want the packaged version with setup instructions: https://hanumant4.gumroad.com/l/caopgu Happy building!

If you've ever built a file upload feature, you've probably hit this: a user uploads a photo from their iPhone, and your app breaks. No preview, no compression, just a silent failure — because the file is .heic and browsers can't read it. HEIC is Apple's default photo format since iOS 11. Every iPhone photo taken today is HEIC. And virtually every JS image compression library just ignores the problem. I spent a weekend building PixSqueeze to fix that. The problem with HEIC in the browser Browsers use the <canvas> API to compress images. You draw the image onto a canvas, call canvas.toBlob() , and get a compressed file back. Clean, client-side, zero server cost. The problem: canvas.drawImage() only works if the browser can decode the image first. And no major browser can decode HEIC natively — not Chrome, not Firefox, not even Safari on macOS (Safari on iOS can, but that doesn't help your web app). So when a user picks a HEIC file, your image element fires onerror , your canvas stays blank, and your compression pipeline silently does nothing. The solution: server-side conversion, then client-side compression PixSqueeze handles this in two stages: Stage 1 — Server converts the format HEIC (and TIFF, camera RAW) files get sent to a small Express server. The server uses heic-convert running in a dedicated worker thread to convert to JPEG. Worker threads matter here — HEIC decoding is CPU-intensive WASM work, and running it on the main event loop would block every other request. Stage 2 — Client compresses the result The converted JPEG comes back to the browser, where the normal canvas-based compression pipeline takes over. Quality, resize, watermark hooks — all the usual options. The detection is important too. You can't just check file.type — iOS often sets HEIC files with an empty MIME type. PixSqueeze checks the ISO Base Media File Format magic bytes directly: async function isHeicFile ( file ) { const buffer = await file . slice ( 0 , 12 ). arrayBuffer (); const byt

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GitHub热门项目 | Open source RabbitMQ: core server and tier 1 (built-in) plugins | Stars: 13,696 | 3 stars today | 语言: JavaScript

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