Automate AI Skills with npx autoskills

AI tools have gotten ridiculously good for developers and honestly, if you're not using at least 2-3 of these in your workflow right now, you're leaving free productivity on the table. Here are the tools actually worth your time in 2026: GitHub Copilot — still the best for inline code generation and auto-completion. It just understands context. Cursor — basically VSCode but built for AI. If you're writing Python or JavaScript, this thing is a game changer. LLaMA locally on your machine — zero latency, zero privacy concerns, works even when you're offline. Getting faster every month. Tavy or similar for API testing — generates test cases, mocks, documentation automatically from your code. The game isn't about using one magic tool. It's about building a workflow where these tools handle the repetitive parts so you can actually think. Which one are you using? What should everyone know about? #AITools #DeveloperTools #Productivity #SoftwareDevelopment #AI

A typical developer day looks simple… Until the bugs arrive. Morning: "Today I’ll write clean code." Afternoon: "Why is this not working?" Night: "AI, please save me." But after building more projects, I realized: Real growth doesn’t come from writing perfect code. It comes from: • Reading error messages carefully • Using documentation properly • Taking breaks when stuck • Staying consistent every day • Using AI as a tool — not a shortcut Every developer goes through this cycle. Still learning. Still building. Be honest: Which stage describes your day today? 😄 #webdevelopment #softwareengineering #codinglife #developerhumor #programming #reactjs #ai

AI can write code in seconds…  but most of us are still trapped in endless "vibe coding" loops — prompt, fix, re-prompt, repeat. There’s a better way. Spec-Driven Development (SDD) lets you stay in full control: - You write one clear specification  - AI implements it reliably  - You iterate by simply updating the spec (never fighting the code) Hence, I am announcing this new course https://lnkd.in/dg5z3B4F on SDD! In this practical, hands-on course you’ll go from: → Building CLI tools with precision  → Creating real web apps  → All the way to full-stack projects and production workflows -------- without coding!! ---------- 32 hours • Project-based • Python-focused (Yes!, we need you to review the AI generated code) If you’re a Python developer (2+ years exp) tired of babysitting AI tools, this course will change how you build software. DM me for more details about the course. 👉 https://lnkd.in/dg5z3B4F #SpecDrivenDevelopment #AIForDevelopers #FutureOfCoding #SDDMastery #VibeCoding

𝗡𝗼-𝗰𝗼𝗱𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗿𝗲𝗮𝗹 𝗱𝗲𝗮𝗹. I'm not here to tell you they're a scam or that you need to learn Python before you're allowed to build anything. Make works. n8n works. VAPI works. But there's a conversation nobody's having and that is.. These tools will build exactly what you tell them to. And that's precisely the problem. Why is it so ?? Because most people don't actually know what to tell them. I've sat across enough builders to see where it goes wrong. The agent runs perfectly in testing. Clean demo. Smooth handoffs. Everyone's impressed. Then it meets a real user. Who responds at 2 AM. Who sends a voice note instead of text. Who skips the step you assumed they wouldn't skip. Who gives you data in a format you never anticipated. And suddenly your beautiful no-code workflow has no idea what to do. The tool never failed. The thinking around it did. No-code removes the barrier to building. It doesn't remove the barrier to building the right thing. Judgment isn't a feature you can drag and drop. #AIAgents #aiproductivity #AICoding #AIEngineering #DevTools #AI #Automation #NoCode #FutureTech #StartupCommunity, #feed, #Enterpreneurship

Unpopular opinion: Traditional coding, as we know it, is on borrowed time. We're used to writing line after line of code to tell a machine *how* to do something. But Agentic AI flips this on its head, focusing on *what* you want to achieve. 🤔 Imagine telling an AI, "Build me a web app that tracks my daily habits," and it handles the database, front-end, and deployment without you touching a single line of Python or JavaScript. This isn't just about AI writing snippets; it's about AI owning the entire task execution, from planning to debugging. It's a fundamental shift from instructing to delegating. This means the role of a developer isn't disappearing, but evolving. You become a strategist, an architect of intent, guiding intelligent agents rather than meticulously crafting every function. So, instead of mastering the next framework, perhaps it's time to master prompt engineering, system design, and the art of defining clear objectives for autonomous agents. The value shifts from execution to vision. What's your take on this shift? Are you ready to delegate the code? #AgenticAI #FutureOfCoding #SoftwareDevelopment #AIDevelopment

🔥🚀 AI CHEAT CODE #034 🔥🚀 💥 Cursor 3 just launched a GAME-CHANGER — run MULTIPLE AI agents in parallel and 10x your coding speed! 🤯⚡ 🤖 CURSOR 3's AGENTS WINDOW = Your Personal Dev Army 🪖 Last week, Cursor dropped v3 with a brand-new Agents Window — mission control where you run multiple AI agents SIMULTANEOUSLY across different repos! 🚀🔥 Here's how to unlock it RIGHT NOW 👇 ⚡ Step 1: Update to Cursor v3 → Cmd+Shift+P → "Check for Updates" → install 💻✅ 🔥 Step 2: Open the Agents Window → Cmd+Shift+P → type "Agents Window" → open it 🖥️ 🤖 Step 3: Spawn PARALLEL agents → Click "+" to create new agent tabs → Agent 1 = write tests, Agent 2 = refactor, Agent 3 = write docs 📝 💡 Step 4: Use /best-of-n (NEW COMMAND!) → Runs the SAME task across MULTIPLE models simultaneously → Picks the BEST result automatically! 🏆 🎨 Step 5: Design Mode (INSANE!) → Click directly on any UI element → Annotate it → agent fixes EXACTLY that component — no more vague descriptions! 😂✨ 🎯 PRO TIPS 💪 ✅ Move agents between local → cloud → SSH mid-task 🔁 Cloud agents keep working even with your laptop CLOSED! 📊 Side-by-side grid view to monitor all agents at once ⚡ 72% autocomplete acceptance rate = more shipping 🚢 💬 Drop a comment if this blew your mind! 🤯 👍 LIKE & REPOST to share with your dev squad! 🔥 🔔 FOLLOW Naresh Dawer for daily AI cheat codes! 🔖 SAVE this post — you'll thank yourself later! #AI #DevTools #CursorIDE #Coding #Programming #SoftwareEngineering #AITools #DevOps #WebDevelopment #TechTrends #MachineLearning #Innovation #Automation #ArtificialIntelligence #OpenSource #Python #JavaScript #CloudComputing #ProductivityHacks #TechNews

If you assume AI coding tools perform consistently across your tech stack, this might surprise you. In real-world refactoring tasks, performance varies by more than 8× depending on the programming language: - JavaScript: 31.9% success - C: 3.7% Same models. Completely different outcomes. This helps explain why some teams see substantial productivity gains, while others spend more time on reviews than ever before. The difference isn’t just how you use AI. It’s where you use it. We broke this down in our latest blog post. Read it now to see what this means for your rollout, and how you could rethink AI coding performance: https://lnkd.in/eUeXKVFi #AICoding #LLMPerformance #AISoftwareDevelopment

Unit testing is one of those things the entire industry agrees on in principle but struggles with in practice. Not because teams don't care. The challenge is that writing tests lives outside the natural flow of writing code in a separate file and when timelines get tight, that step slips. So I asked a simple question: what if the test file just showed up the moment you saved your code? No separate step. No context switch. Just there. That's the idea behind SilentSpec. It's a VS Code extension that generates unit tests when you save a TypeScript file. It reads your exports, function signatures, types, and edge cases, then creates a matching test file in the background. It connects directly to your AI provider — github models (default), OpenAI, Anthropic, or Ollama for fully local generation. Auto-detects Jest or Vitest from your project so the output runs without modification. v1.0 is live. Free and open source. What's worked for your team to keep test coverage consistent as a project grows? #vscode #typescript #testing #softwareengineering #developers #opensource #buildinpublic #AI

After months of using AI code editors, here's my honest take 👇 I've tried them all — and not every tool deserves the hype. Here's what I actually found: Claude — The best of the bunch. Understands context deeply, writes clean code, and handles debugging like a pro. My first stop for any complex problem. Cursor — Feature-rich and powerful, but sometimes misses the point when diagnosing issues. Debugging is good, but it's a memory hog that slows down your whole system — that's why I keep it as a separate IDE. OpenCode — Genuinely impressive. Great at understanding, editing, and spotting issues. The one downside? It's slow. Like, painfully slow. Kills the flow. Roo Code — Not great for big codebases. Coding quality is hit or miss, and it tends to do things unrelated to what you asked. Best for simple, contained tasks. Codex — Fine for small projects, but don't bring it anywhere near a large codebase. It loses context fast. The verdict? If I had to pick one: Claude. Hands down. What AI tools are you using for coding? Drop them below 👇 #AITools #CodingTools #DeveloperExperience #Claude #Cursor #OpenCode #SoftwareDevelopment #Programming

Everyone’s building AI agents. Almost nobody has a good way to debug why they fail. You run a workflow, get a bad output, tweak a prompt, try again, and hope for the best. That loop gets messy fast. So I built the fix. 🛠️ EvalForge is a lightweight tool for testing and debugging AI agent workflows. Load a benchmark, run a planner → worker → reviewer pipeline, and instantly see which tasks passed, which were flagged, and where things broke. Why I built it: A lot of agent demos look impressive when they work once. What’s much harder ? What's way more useful? is understanding why they fail consistently. With EvalForge, you can: ✅ Upload a benchmark or load sample tasks ✅ Run an eval flow in seconds ✅ Filter passed vs flagged tasks ✅ Inspect stage-by-stage traces ✅ Compare outcomes and spot failure patterns fast Built for the OpenAI X Handshake Codex Creater Challenge, and honestly, this was such a fun reminder that AI can speed up building, but product thinking, iteration, and scoping still matter just as much. Stack: Next.js · TypeScript · FastAPI · Python · Render · Vercel You can try EvalForge here: https://lnkd.in/gcK_8unR And if you check it out, comment what I should improve or change to make it even better for the challenge requirements 👇 #OpenAI #Codex #Handshake #AIShowcase #BuildInPublic #AIAgents #SoftwareEngineering #FullStackDevelopment #Nextjs #FastAPI #StudentBuilder #TechProjects