Elke Heymans’ Post

𝐂𝐥𝐚𝐮𝐝𝐞 𝐂𝐨𝐝𝐞 is NOT a Copilot. It’s a 𝟏𝟎𝐗 shift. 🚀 Claude Code is not a copilot. Not even close. A Copilot lives inside your 𝐈𝐃𝐄. It sees your open file. It suggests the next line or fills in a function. That’s where its power ends—because that’s where its access ends. Claude Code runs in your terminal. That one difference changes everything. Because it’s a CLI tool, it has the same access to your system that you do. It doesn't just suggest what you should do; it actually does it. A Real-World Example: The "𝐁𝐫𝐨𝐤𝐞𝐧 𝐏𝐫𝐨𝐱𝐲" Test 🛠️ My local Python proxy server went down. I didn't spend 20 minutes debugging. I typed one sentence: "𝙈𝙮 𝙥𝙧𝙤𝙭𝙮 𝙞𝙨𝙣'𝙩 𝙬𝙤𝙧𝙠𝙞𝙣𝙜." Here is what Claude Code did, on its own, in sequence: ✔ Isolated the problem by creating custom test cases. ✔ Generated log files and parsed them instantly. ✔ Wrote Regex to extract specific error patterns from those logs. ✔ Modified the source code based on the findings. ✔ Restarted the proxy. 𝐒𝐭𝐢𝐥𝐥 𝐛𝐫𝐨𝐤𝐞𝐧? 𝐈𝐭 𝐝𝐢𝐝𝐧'𝐭 𝐠𝐢𝐯𝐞 𝐮𝐩. 𝐈𝐭 𝐤𝐞𝐩𝐭 𝐠𝐨𝐢𝐧𝐠: ✔ Checked ports and looked at running processes. ✔ Identified a stale process from a previous session blocking the port. ✔ Killed the process and restarted the server. ✔Reran its own test cases to verify the fix. Everything worked. ✅ 𝐓𝐡𝐞 𝐁𝐨𝐭𝐭𝐨𝐦 𝐋𝐢𝐧𝐞 💡 A Copilot would have autocompleted a line in my config file and wished me luck. Claude Code solved the problem. It isn't trapped in an editor tab. It can investigate, act, fail, adjust, and try again. It can: ★ Read/Write files 📁 ★ Run shell commands & scripts 💻 ★ Inspect ports and processes 🔍 ★ Parse logs and restart services 🔄 and much more .. Copilots suggest code. Claude Code solves problems. That’s not a small difference—it’s the future of engineering. #ClaudeCode #AI #SoftwareEngineering #CLI #CodingAutomation #DevTools #Programming #ArtificialIntelligence

Anthropic accidentally leaked the entire Claude Code source. 41K+ forks in hours. 8,000 DMCA takedowns filed. But here's what nobody is talking about. Devs found hidden feature flags buried in the code that reveal exactly where Claude Code is heading: PROACTIVE - autonomous agent mode. Claude doesn't wait for your commands. It acts on its own. KAIROS - context-aware triggering. Claude knows WHEN to step in. DAEMON - persistent background process. Claude runs while you sleep. AGENT_TRIGGERS - event-driven multi-agent teams that message each other. Read that list again. That's not a coding assistant. That's a self-driving software team. I've been building multi-agent systems with Claude for months now. Sub-agents, orchestration, MCP tools chaining together. What these flags tell me is that Anthropic is baking all of it into the core product. The gap between "coding assistant" and "autonomous developer" is closing faster than anyone expected. A Korean dev rewrote the entire codebase in Python in 8 hours. Hit 100K GitHub stars. The genie is out of the bottle. The builders who understand agent architecture today will have a massive head start tomorrow. Which feature flag are you most excited about? #ClaudeCode #AIAgents #Anthropic

Shipped something I've been quietly working on. Today I'm open-sourcing the Playwright Autopilot Skill — a drop-in skill for Claude Code, Cursor, Copilot, and other major coding agents that gives them full browser autopilot capability. The agent follows a structured loop: Navigate → Observe → Identify Elements → Act via MCP → Verify with Screenshot → Translate to Python (or retry on failure) → Save and run No MCP server setup. No boilerplate. Just drop the skill file into your project. What I wanted to solve: agents that can write Playwright code but can't actually drive a browser end-to-end. This closes that gap, across agents and projects. Repo: https://lnkd.in/dYY7Akjh If you're working with AI agents in your dev workflow, I'd genuinely appreciate your feedback. #ClaudeCode #Playwright #AIAgents #OpenSource #BrowserAutomation

Cool stuff. “I wasted 68 minutes a day re‑explaining my code.” That line itself made me stop scrolling. This article shows how a common developer frustration led to building an auto‑memory system to preserve context and reduce repetitive explanations to copilot cli. A powerful reminder that the best tools often come from lived problems.

🚀 AI CHEAT CODE #026 🚀 Most devs use GitHub Copilot wrong. Here's the trick that 10x'd my output 👇 Stop accepting single-line suggestions. Use Copilot Chat with THIS prompt pattern: Step 1: Highlight your entire function Step 2: Open Copilot Chat (Ctrl+I) Step 3: Type: "Refactor this for readability, add error handling, and write unit tests" You just got 3 tasks done in 10 seconds. 🤯 Step 4: Ask: "What edge cases am I missing?" Step 5: Paste those edge cases directly into your test file ⚡ Pro Tip: Add your tech stack to the prompt: "Refactor for Python 3.11 with FastAPI best practices" — Copilot tailors everything perfectly. Drop a 🔥 if this saved you time today! Save this post for your next code review session. #AI #GitHubCopilot #Coding #DevProductivity #SoftwareEngineering #Python #CloudComputing #DevOps

I got rate-limited by GitHub Copilot for 48 hours. Best thing that could've happened. It forced me to actually test what I'd been meaning to test. Same codebase. Same prompts. Same objectives. Three different AI coding setups, side by side. What I found surprised me. Claude Code on Sonnet 4.6 via API consistently beat my Copilot setup running Opus. Not by a little. It was more precise across multi-step changes and better at holding intent across an entire system — not just the file in front of it. Smaller model on paper. Heavier in practice. Then I ran the same work through OpenAI Codex with GPT-5.4. Different animal. Slightly pricier than Claude Code, slower, but more creative — especially in UI generation and producing code that reads like a human wrote it. It thinks before it answers, and you can feel it. The real lesson isn't "Model A beats Model B." It's assistant extension A beats assistant extension B all to heck, even when B is using a more capable model. We're heading toward a world where one model is your optimizer, one is your architect, and one is your storyteller — and the IDE is the control panel, but the control plane, the app slinging prompts for you is at least half the battle. And VS Code quietly became the trading floor. The extensions are as big a part of the market there as the actual models. Choose wisely. #AI #SoftwareEngineering #VSCode #GitHubCopilot #ClaudeAI #OpenAI #DeveloperTools #AIAssistedDevelopment

With all the AI FOMO right now, it feels like everyone is rushing to generate code rather than understand it. But if you still appreciate the "old style" of actually getting your hands dirty to learn the mechanics of a language—specifically Rust—the tutorial fatigue is real. Watching hours of videos just isn't the same as building something yourself. That's why I put together something for those of us who learn best by doing: Rust Learning Bytes. 🦀 Inspired by platforms like CodeCrafters and the awesome "Build your own X" repositories, I wanted to create a hands-on path to picking up Rust. Instead of just handing you the final code, each file in the repo includes helpful comments formatted like feature tickets. They give you just enough of a hint to nudge you toward the solution without ruining the "aha!" moment of figuring it out. 💻 You can jump into the repo and start building here: https://lnkd.in/eTuFxwzC If you get stuck or want a deeper dive into the "why" behind the code, I'm also writing a companion blog series called "Rust by Doing - Build your own X." I'll be posting walkthroughs for each section there. 📝 Check it out here: https://suhird.me/series I'd love to hear what you think. Let me know in the comments, or feel free to open a GitHub Issue or PR if you spot any mistakes or have ideas for new chapters I should add. Happy coding! 🚀 #Rust #RustLang #SoftwareEngineering #LearnByDoing #Programming #OpenSource

After watching Iron Man, every kid wanted their own JARVIS. In 2008, that was just a dream. In 2026, you can actually build it. I spent the last few weeks building Jarvis v1 from scratch :- a production-grade AI agent with: → A LangGraph state machine at the core → 6 tools: web search, calculator, file I/O, code execution, and memory → 3 layers of memory: user profile, project context, and chat history → Smart guardrails: loop detection, code security review, human-in-the-loop → Parallel tool execution, independent tasks run simultaneously, not one by one No magic abstractions. No copy-pasting tutorials. Every component built to be understood, not just shipped. And this is just v1. v2 brings multi-agent architecture (specialist agents for research, coding, writing), a React web UI with streaming responses, RAG memory, Docker sandboxed code execution, and a self-improving agent that writes its own tools. The best part? It's fully open source. Clone it, customise it, make it yours. Swipe to see how it works 👉 GitHub: https://lnkd.in/gigEC6GR #AIAgents #LangChain #LangGraph #Python #BuildInPublic #OpenSource

Stop writing PR descriptions manually. The Eulogik team built AutoPR to automate it entirely using 100% free AI models. Here's the problem every developer knows: ❌ Writing PR descriptions is tedious and time-consuming ❌ Manual code reviews are slow and inconsistent   ❌ Context-switching from coding to docs kills flow So we built AutoPR – an open-source CLI + GitHub Action that: ✅ Auto-generates professional PR descriptions from git diffs ✅ AI-powered code reviews with security/performance checks ✅ Runs 100% FREE via OpenRouter (Gemma, Llama, Mistral) ✅ Works as CLI: `autopr generate --no-dry-run` ✅ Or fully automated GitHub Action on every PR Built with TypeScript strict mode, 22 passing tests, zero bloat. MIT license – free to use and modify. 🔗 GitHub: https://lnkd.in/dNt4UiYH 📦 npm: npm install -g @eulogik/autopr Would you use this? Drop a ⭐ if you're tired of writing PR descriptions! #OpenSource #GitHub #AI #CodeReview #DevTools #Automation #TypeScript #DeveloperProductivity #LLM #GitHubActions #Coding

Most developers write their AI assistant rules files once, by hand, and never touch them again. They're generic. They're stale. And if you use more than one AI coding tool, you're maintaining duplicates that slowly drift apart. I built @rulesgen/rulesgen to fix that. It analyzes your actual codebase — frameworks, dependencies, naming patterns, async style, test setup, even recent git history — and auto-generates optimized rules files for: ✅ Claude Code (CLAUDE.md) ✅ Cursor (.cursorrules) ✅ GitHub Copilot (copilot-instructions.md) ✅ Windsurf (.windsurfrules) All from a single command. All tuned to your specific project, not a boilerplate. Supports JS/TS, Go, Python, monorepos, Docker, Terraform, GitHub Actions — and 50+ frameworks out of the box. Get started: npx @rulesgen/rulesgen generate Open source. MIT licensed. Available on npm now. Would love feedback from anyone deep in the AI-assisted dev workflow 🙏 #AITools #DevTools #ClaudeCode #Cursor #GitHubCopilot #buildinginpublic #OpenSource