AI-assisted coding and automation in modern development

💬 “I love coding.” 💬 “Me too!” …but which coding are we talking about? 😏 Today, coding isn’t just about writing lines of code — it’s about the ecosystem you build around it. ✨ AI models ✨ IDEs like VS Code ✨ Python & automation ✨ GitHub, Docker, CI/CD ✨ Prompt engineering & agent frameworks Modern developers don’t code alone anymore. They collaborate with AI copilots, automate workflows, deploy faster, and focus more on problem-solving than syntax. 💡 The real flex in 2026 isn’t knowing one language — it’s knowing how to combine tools, AI, and systems to build something powerful. If you love coding today, chances are you also love: 🤝 AI-assisted development ⚙️ Automation 🚀 Shipping faster with smarter tools Tag someone who says “I love coding” — but really means this 😄 #CodingLife #Developers #AIinTech #Programming #SoftwareEngineering #TechHumor #AIDevelopment #FutureOfCoding #VSCode #Python

💬 “I love coding.” 💬 “Me too!” …but which coding are we talking about? 😏 Today, coding isn’t just about writing lines of code — it’s about the ecosystem you build around it. ✨ AI models ✨ IDEs like VS Code ✨ Python & automation ✨ GitHub, Docker, CI/CD ✨ Prompt engineering & agent frameworks Modern developers don’t code alone anymore. They collaborate with AI copilots, automate workflows, deploy faster, and focus more on problem-solving than syntax. 💡 The real flex in 2026 isn’t knowing one language — it’s knowing how to combine tools, AI, and systems to build something powerful. If you love coding today, chances are you also love: 🤝 AI-assisted development ⚙️ Automation 🚀 Shipping faster with smarter tools Tag someone who says “I love coding” — but really means this 😄 #CodingLife #Developers #AIinTech #Programming #SoftwareEngineering #TechHumor #AIDevelopment #FutureOfCoding #VSCode #Python

🍷 𝗙𝗿𝗼𝗺 𝗽𝗼𝘀𝘁-𝗱𝗶𝗻𝗻𝗲𝗿 𝗶𝗱𝗲𝗮 𝘁𝗼 𝗳𝘂𝗹𝗹 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁: 𝗝𝘂𝘀𝘁 𝗵𝗼𝘄 𝗴𝗼𝗼𝗱 𝗶𝘀 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄? Yesterday after dinner, I gave myself a challenge: Is it really possible to build and deploy a working application without being a professional software developer, relying purely on AI? I started with a wonderfully simple prompt to Claude: "𝘐 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘸𝘳𝘪𝘵𝘦 𝘢 𝘱𝘳𝘰𝘨𝘳𝘢𝘮 𝘪𝘯 𝘗𝘺𝘵𝘩𝘰𝘯 𝘵𝘩𝘢𝘵 𝘮𝘰𝘯𝘪𝘵𝘰𝘳𝘴 𝘵𝘩𝘦 𝘱𝘳𝘪𝘤𝘦 𝘰𝘧 𝘢 𝘸𝘪𝘯𝘦 𝘢𝘯𝘥 𝘴𝘵𝘰𝘳𝘦𝘴 𝘵𝘩𝘦 𝘥𝘢𝘪𝘭𝘺 𝘱𝘳𝘪𝘤𝘦 𝘪𝘯 𝘢 𝘧𝘪𝘭𝘦 - 𝘤𝘢𝘯 𝘺𝘰𝘶 𝘩𝘦𝘭𝘱 𝘮𝘦?" Claude asked a few clarifying questions, and then we got to work. And by "𝘸𝘦," I mean things escalated quickly. 🚀 Before my head hit the pillow, I didn't just have a simple script. I had: ✅ Code under version control on GitHub ✅ Automated tests (which Claude proactively ran every time we tweaked something) ✅ A Docker container running smoothly on my Raspberry Pi ✅ A cron job checking the wine prices daily Ironically, the hardest part of the entire evening wasn't writing the Python code. It was the "𝘱𝘭𝘶𝘮𝘣𝘪𝘯𝘨"—wrestling with the data flow from InfluxDB, through Grafana, and finally getting it to display beautifully on my Home Assistant. That took the lion's share of the effort! 🦁 𝗧𝗵𝗲 𝘃𝗲𝗿𝗱𝗶𝗰𝘁: Could someone with absolutely zero programming or systems knowledge pull this off today? Honestly, not the solution i arrived at, with home assistant and all. You still need to understand how the puzzle pieces fit together 🧩, however a simpler solution giving you a notification of change in price - no problem at all. But... the capabilities are evolving at a breakneck pace. I have no doubt that in the very near future, this level of creation will be accessible to everyone. The barrier to entry is crumbling. If you haven't tried building something with AI yet, find a simple problem, open a prompt, and see where it takes you. 𝘗.𝘚. 𝘛𝘩𝘪𝘴 𝘱𝘰𝘴𝘵 𝘸𝘢𝘴 𝘥𝘳𝘢𝘧𝘵𝘦𝘥 𝘸𝘪𝘵𝘩 𝘮𝘺 𝘈𝘐 𝘸𝘪𝘯𝘨𝘮𝘢𝘯, 𝘐𝘤𝘦𝘮𝘢𝘯 (𝘰𝘵𝘩𝘦𝘳𝘸𝘪𝘴𝘦 𝘬𝘯𝘰𝘸𝘯 𝘢𝘴 𝘎𝘦𝘮𝘪𝘯𝘪—𝘵𝘢𝘭𝘬 𝘢𝘣𝘰𝘶𝘵 𝘢𝘯𝘵𝘩𝘳𝘰𝘱𝘰𝘮𝘰𝘳𝘱𝘩𝘪𝘻𝘢𝘵𝘪𝘰𝘯!). 𝘔𝘢𝘷𝘦𝘳𝘪𝘤𝘬 𝘰𝘶𝘵. ✈️😎 #AI #ClaudeCode #HomeAssistant #Python #ContinuousLearning #FutureOfTech #NoCode

🚀 Claude Code: The AI Coding Assistant Most Developers Are Missing Many developers think AI tools only generate code. But Claude Code goes much deeper. It can: ✔ Read your entire codebase ✔ Edit files directly in your project ✔ Refactor modules ✔ Generate tests and documentation ✔ Debug using full project context In this carousel, I break down: • What Claude Code actually is • How to install and use it • Where it helps the most • Where you should still rely on your own knowledge AI won’t replace developers - but developers who use AI effectively will move much faster. Swipe through to learn how to use it properly. 👇 #AI #Python #Coding #Developers #SoftwareDevelopment #ArtificialIntelligence #Programming #TechLearning #DeveloperTools

🚀 AI Meets JetBrains IDEs: A Game-Changer for Developers! 🚀 I’m thrilled to share that Cursor is now available in JetBrains IDEs like IntelliJ IDEA, PyCharm, WebStorm, and more via the Agent Client Protocol (ACP)! 🏆 For developers, this is HUGE: ✨ Frontier AI Models Right in Your IDE Use models from OpenAI, Anthropic, Google, and Cursor directly while coding no context switching needed! ⚡ Agent-Driven Development Get AI-powered suggestions, auto-completion, and refactoring help without leaving your IDE. 🔍 Deep Code Intelligence Cursor ACP brings secure codebase indexing and semantic search, making large enterprise projects easier to understand and navigate. 💡 Why this excites me: Whether it’s solving tricky Java algorithms, debugging Python scripts, or building multi-language apps, this feels like having a coding partner who never sleeps and always gives smarter suggestions. 🔥 Pro Tip for fellow developers: Install Cursor ACP directly in your JetBrains AI chat and authenticate with your existing Cursor account. It’s free for all paid plans and integrates seamlessly! 🔗 More info: https://lnkd.in/eKd75XVX I’m curious to hear from my network: If you had an AI coding partner in your IDE, what would you have it do first? 🤔👇 Comment below I want to hear your ideas and experiments! #AI #JetBrains #Cursor #SoftwareDevelopment #Java #Python #WebDevelopment #Coding #DeveloperTools #Productivity #TechCommunity #FutureOfCoding

“Vibe Coding” Is Triggering Developers… And That’s The Point. Everyone is debating: “Real engineers don’t vibe code.” “If you don’t understand every line, you’re not a developer.” But here’s the uncomfortable truth: The future doesn’t reward the one who types the most code. It rewards the one who ships the most value. We moved from: Assembly → C → Python → Frameworks → No-Code Now we’re moving to: AI + Vibe Coding And suddenly… it’s a problem? Let’s be honest: Most production code is Googled. Most developers use StackOverflow. Now we use AI. So what changed? Ego. Vibe coding isn’t about “not knowing code.” It’s about: ✅ Thinking in systems ✅ Understanding architecture ✅ Prompting intelligently ✅ Iterating fast ✅ Shipping faster than ever before The developer who adapts wins. The one who resists… tweets. In 5 years, nobody will ask: “Did you write every line manually?” They’ll ask: “What did you build?” I’m betting on builders. Mahesh Dubey #VibeCoding #AIRevolution #FutureOfCoding #Developers #PromptEngineering #TechDebate #BuildInPublic

Just published: my retrospective on shipping a production C/C++/C#/Python numerical library in 2 days instead of the estimated 3 weeks using spec-driven development. The twist? It was my first project with C# and pybind11. I wrote neither by hand. We used to program in assembly, then C, then Python. Now we're programming in Markdown. Key takeaways from the article: - Spec-driven development treats specifications as executable documents — detailed enough that an AI agent can implement directly from them - The workflow has two phases: iterative planning (producing a spec) and agent-driven implementation — with explicit feedback loops between them - When you think you're done planning, you're not. A fresh-session cross-check catches inconsistencies both you and the AI will miss - Speed without review produces technical debt at 10× the normal rate. Active review is non-negotiable - AI agents start fresh every session. Engineers who can navigate ambiguity and write clear specs become force multipliers; those waiting for well-scoped tickets may find themselves competing with agents The productivity gain is real but nuanced: you're 10× faster at writing code, while spending more time on what actually matters — planning, reviewing, and making judgment calls. Would love to hear your experiences! #SpecDrivenDevelopment #AIinSoftware #GitHubCopilot #SoftwareEngineering #AgenticAI #DevOps

I just submitted a session proposal for JSpring on AI Coding — and honestly, I have no idea what tools, models, or methods I'll be using by then. That's kind of the point. The space is moving so fast that any talk I write today would be outdated by the time I give it. So instead of a prepared lecture, I want to host an open conversation: what tools are you using? How are you structuring your prompts? Are you doing Spec Driven Development or going with something else? Whether my session gets accepted or not — if you're going to JSpring, let's talk AI coding. Would love to connect. #JSpring #AICoding #Java #SoftwareDevelopment

Built. Shipped. Done. Today I’m shipping a production-ready self-improving coding agent. What it does: - Writes Python code from natural language goals - Generates tests automatically - Executes safely in a sandboxed environment - Learns from failures and reflects before retrying - Remembers successful patterns across sessions - Stops itself when stuck to prevent waste The full stack: • Agent loop with Claude API • Production sandbox (CPU/memory/file limits) • Test runner with unittest integration • Short-term memory (last 5 attempts) • Long-term memory (persistent solutions) • Circuit breaker (stops after 3 failures) • Rate limiter (20 requests/minute) • Advanced safety (detects infinite loops) Today I added the unglamorous but critical stuff: - Professional CLI with argparse - Built-in demo mode - Stats command - Example library - Comprehensive documentation - Error handling - Help text Try it yourself: python3 main.py –demo The difference between the first day I started and the last day isn’t just features. It’s maturity. Day 1: It works on my machine Final Day: Anyone can install and use it in 5 minutes What I learned: Building teaches more than any course Documentation matters as much as code Safety systems aren’t optional Polish is what makes it production-ready Shipping beats perfecting This project taught me more about AI engineering The agent isn’t perfect. It’s not going to replace developers. But it’s real, it works, and it’s mine😁 Most importantly: I understand the line That’s the difference between consuming content and creating systems. Next: Taking these learnings into my next build. GitHub link: https://lnkd.in/eKzhDWnM To everyone still in tutorial hell: just start building. Pick something that scares you a little and build it in public Seven days from now, you’ll have something real to show. #BuildInPublic #AI #Python #MachineLearning #SoftwareDevelopment #Completed

Vibe coding just broke my codebase. Not because the model can't write Python, because it forgot the architecture existed halfway through the refactor. Last year, “vibe coding” was enough. This year, my projects started breaking for a different reason. Not because the model couldn’t write code. Because it couldn’t hold the whole system in its head long enough to finish the job. That’s why Z.ai’s GLM-5 announcement caught my attention. Not the hype. The engineering choices. GLM-5 scales from 355B to 744B parameters. But the more interesting number is this: 40B active parameters. That’s a very specific bet on “big brain, sane runtime.” Then they go a level deeper. They add DeepSeek Sparse Attention to keep long context without turning inference into a bonfire. And they admit the part everyone feels but few say out loud: RL is where competence becomes excellence… …except RL at LLM scale is painfully inefficient. So they built “slime,” an async RL setup to iterate faster. Lesson for builders: We’re moving from “prompting output” to “operating systems.” If your model can’t plan, remember, and recover… You don’t have an agent. You have autocomplete with ambition. What convinced me this wasn’t just branding? They’re showing “long-horizon” results that look like real work. Vending Bench 2 (1-year simulated business): GLM-5 ends at $4,432. That’s not a cute demo. That’s budgeting, inventory, compounding decisions, and not self-sabotaging over time. On SWE-bench Verified: 77.8. Not #1 overall, but firmly in the tier where “ship this” starts becoming realistic. Then they make the product move I’ve been waiting for: Document-native output. Not “here’s text, go format it.” Direct .docx / .pdf / .xlsx deliverables. That’s how you turn models into something teams can trust. Actionable advice (how I’m adapting my workflow this quarter): • I’m splitting work into 2 loops Loop 1: Plan + constraints Loop 2: Execute + verification • I’m treating context like a budget If the agent can’t manage it, I add explicit state Decisions, assumptions, open questions, next steps • I’m benchmarking agents on “finish rate,” not “first draft quality” Did it ship a usable artifact Did it survive edge cases Did it keep the system consistent • I’m prioritizing open weights with permissive licensing for internal tools GLM-5 shipping under MIT is a signal It changes what you can safely build If you’re building agents, this is the shift: Chat is cheap. Completion is expensive. And the winners will be the teams that engineer for completion. What’s the biggest thing your current “agent” still can’t reliably finish end-to-end? ♻️ Repost to update your network on the Agentic future. + Follow me for more updates on AI Agents and Automation. Link in the first comment ⬇️ #AI #Agents #LLM #OpenSource #DeveloperTools #Productivity #SoftwareEngineerin