AI in Coding and Development

I’ve been running a quiet experiment: using AI coding (Vibe Coding) across 10 different closed-loop production projects — from minor refactors to major migrations. In each, I varied the level of AI involvement, from 10% to 80%. Here’s what I found: The sweet spot? 40–55% AI involvement. Enough to accelerate repetitive or structural work, but not so much that the codebase starts to hallucinate or drift. Where AI shines: - Boilerplate and framework code - Large-scale refactors - Migration scaffolds - Test case generation Where it stumbles: - Complex logic paths - Context-heavy features - Anything requiring real systems thinking [and new architectures etc]. - Anything stateful or edge-case-heavy I tracked bugs and % of total dev time spent fixing AI-generated code across each project. Here's the chart. My learning is that: overreliance on AI doesn’t just plateau, it backfires. AI doesn't write perfect code. The future is a collaboration, not a handoff. Would love to hear how others are navigating this balance. #LLM #VibeCoding #AI #DeveloperTools #Dev

90% of engineers using AI coding tools are doing it wrong. They're treating AI like a code monkey. Fire prompt → Get code → Accept all changes → Ship. That's why we see 128k-line AI pull requests that became memes (look this up, it's a fun read). After spending quite a bit of time using AI dev tools, I discovered the real game isn't about generating more code faster. It's about rapid engineering while managing cognitive load. My workflow now: 1. Start with AI-generated system diagrams 2. Ask questions until I understand the architecture 3. Create detailed change plans 4. Break down into AI-manageable chunks 5. Maintain context throughout This isn't coding. It's orchestration. The best engineers aren't typing anymore. They're conducting symphonies of AI agents, each handling specific complexity while the human maintains the vision. Think about it → We're moving from IDEs to "Cognitive Load Managers." Tools that auto-generate documentation, visualize dependencies in real-time, and explain impact before you commit. The future isn't AI writing code. It's AI helping you understand what code to write. The billion-dollar opportunity? Build the tool that turns every engineer into a systems architect who happens to code. We're not being replaced. We're being promoted. Who else sees this shift? #AI #SoftwareEngineering #DevTools #FutureOfCoding #TechLeadership

🧠 12 open-source GenAI tools that actually deliver (and scale) Not every tool with a GitHub repo deserves your trust. These ones do. 👉 If you're building real GenAI systems—not just demos—save this list. I grouped them into Build, Orchestrate, and Monitor so you know when to use what. GenAI AgentOS: (NEW) 📎 Agent registry → memory handoff → orchestration layer → HITL toggle ✅ Focused on production reliability and audit trails ⭐ https://lnkd.in/gyzMnnjw 🔧 BUILD – For devs building GenAI-powered apps LangChain – The Swiss army knife for chains, RAG, agents, and tools. ⭐ 70k+ stars | https://lnkd.in/gun-rmdj LlamaIndex – Clean integration layer between LLMs and your data. Great for structured docs + flexible vector backends ⭐ 30k+ stars | https://lnkd.in/gW-iBKR2 Flowise – Drag-and-drop LLM orchestration (perfect for demos & MVPs) UI-first, deploy fast, iterate even faster ⭐ 19k+ stars | https://lnkd.in/gA8J3Tr5 Embedchain – Minimalist RAG framework that just works Perfect if you’re tired of config overkill ⭐ 8.5k+ stars | https://lnkd.in/g8DnHQg2 RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. 🔁 ORCHESTRATE – For managing agents, workflows & system logic LangGraph – Declarative, stateful agent workflows built on top of LangChain Role-based agents + memory + edge control ⭐ 2.5k+ stars | https://lnkd.in/gveKVfE4 Superagent – Plug-and-play LLM agent framework API + UI, works with OpenAI, Claude, Mistral ⭐ 5.5k+ stars | https://lnkd.in/gtsy5CQ3 CrewAI – Multi-agent task planning + collaboration Gives each agent purpose, tool access, and autonomy ⭐ 9k+ stars | https://lnkd.in/gUpwvbn9 📊 MONITOR – For logging, debugging, and scaling safely Langfuse – Logging, tracing, and evals for GenAI pipelines Inspect every token and decision ⭐ 4.5k+ stars | https://lnkd.in/g6BEnVyA Phoenix – Open-source observability for LLM workflows Error tracking, token usage, monitoring ⭐ 3k+ stars | https://lnkd.in/gT3ERHgm PromptLayer – Prompt logging + analytics Simple but powerful tracking for prompt performance ⭐ 4k+ stars | https://lnkd.in/gGSRRBrH Helicone – Open-source alternative to OpenAI’s usage dashboard Understand cost, latency, and user behavior ⭐ 6k+ stars | https://lnkd.in/gCgcy7Kd 🔍 Why these matter: Too many GenAI teams waste time gluing together 20 tools, only to discover they can’t scale. These 12 tools are: ✅ Well-maintained ✅ Actively used in production ✅ Community-supported ✅ Actually helpful when you go beyond a chatbot Don’t just play with LLMs. Build systems that can grow. 🔖 Save this. ♻️ Repost this.

𝗧𝗵𝗲 𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗧𝗿𝗮𝗽. This image is painfully accurate. Waterfall gives you structure, but often too slowly. Agile gives you increments, but still needs direction. AI-assisted building gives you prototypes at insane speed. But vibe coding without process and direction? That is chaos with a nice UI. The problem is not that AI writes bad code. The problem is that AI writes convincing code: → It runs. → It looks polished. → It feels like progress. Until you need to deploy it, secure it, monitor it, debug it, scale it, or hand it to another engineer. Then the truth shows up. You did not build a product. You built a pile of assumptions. This is where most AI coding hype gets it wrong. AI makes the first version dramatically easier. But production software still needs: → Architecture → Testing → Security → Ownership → Observability → Documentation → Deployment discipline AI does not make engineering judgment less important. It makes engineering judgment the bottleneck. The critical skill is not just writing code faster. It is knowing what to accept, what to reject, and how to turn AI-generated speed into reliable systems. ↓ 𝗜𝗳 𝘁𝗵𝗶𝘀 𝘄𝗮𝘀 𝘂𝘀𝗲𝗳𝘂𝗹, 𝘆𝗼𝘂 𝗺𝗮𝘆 𝗮𝗹𝘀𝗼 𝗲𝗻𝗷𝗼𝘆 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

Generative AI (GenAI) is transforming DevOps by addressing inefficiencies, reducing manual effort, and driving innovation. Here's a practical breakdown of where and how GenAI shines in the DevOps lifecycle—and how you can start implementing it.  Key Applications of GenAI in DevOps  𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀   - Automatically generate well-defined 𝘂𝘀𝗲𝗿 𝘀𝘁𝗼𝗿𝗶𝗲𝘀 and documentation from business requests.   - Translate technical specifications into simple, 𝗵𝘂𝗺𝗮𝗻-𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 to improve clarity across teams.  𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁   - Automate 𝗯𝗼𝗶𝗹𝗲𝗿𝗽𝗹𝗮𝘁𝗲 𝗰𝗼𝗱𝗲 generation and unit test creation to save time.   - Assist in debugging by analyzing 𝗰𝗼𝗱𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 and suggesting potential fixes.  𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁   - Generate test cases from 𝘂𝘀𝗲𝗿 𝘀𝘁𝗼𝗿𝗶𝗲𝘀 𝗮𝗻𝗱 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 to ensure robust testing coverage.   - Automate deployment pipelines and 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝘃𝗶𝘀𝗶𝗼𝗻𝗶𝗻𝗴, reducing errors and deployment times.  𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀   - Analyze 𝗹𝗼𝗴 𝗱𝗮𝘁𝗮 in real-time to identify potential issues before they escalate.   - Provide actionable insights and 𝗵𝗲𝗮𝗹𝘁𝗵 𝘀𝘂𝗺𝗺𝗮𝗿𝗶𝗲𝘀 of systems to keep teams informed.  How To Implement GenAI: A Step-by-Step Approach  𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗮𝗶𝗻 𝗣𝗼𝗶𝗻𝘁𝘀   Start by pinpointing 𝘁𝗶𝗺𝗲-𝗰𝗼𝗻𝘀𝘂𝗺𝗶𝗻𝗴, 𝗿𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲, 𝗼𝗿 𝗲𝗿𝗿𝗼𝗿-𝗽𝗿𝗼𝗻𝗲 𝘁𝗮𝘀𝗸𝘀 in your DevOps workflow. Focus on areas where GenAI can deliver measurable value.  𝗖𝗵𝗼𝗼𝘀𝗲 𝗧𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗧𝗼𝗼𝗹𝘀   Explore GenAI solutions tailored for DevOps use cases. Look for tools that integrate seamlessly with your existing CI/CD pipelines, testing frameworks, and monitoring tools.  𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻   Ensure your data is 𝗰𝗹𝗲𝗮𝗻, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱, 𝗮𝗻𝗱 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 to the GenAI models you're implementing. Poor data quality can hinder GenAI's performance.  𝗣𝗶𝗹𝗼𝘁 𝗦𝗺𝗮𝗹𝗹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀   Start with a 𝘀𝗶𝗻𝗴𝗹𝗲 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 in a controlled environment. Measure the outcomes and gather feedback before scaling up across your organization.  𝗠𝗼𝗻𝗶𝘁𝗼𝗿 & 𝗥𝗲𝗳𝗶𝗻𝗲   Continuously evaluate your GenAI implementation for accuracy, efficiency, and impact. Be ready to retrain models and refine your approach as needed.  𝗧𝗵𝗲 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀  ✅ Faster development and deployment cycles.   ✅ Improved collaboration through simplified communication.   ✅ Enhanced system reliability with proactive monitoring.   ✅ Reduced manual effort, enabling teams to focus on innovation.  By adopting GenAI in DevOps strategically, you can unlock its potential to create a faster, more efficient, and innovative development environment.  𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝘁𝗮𝗸𝗲?   How do you see GenAI reshaping the future of DevOps in your organization?

Another year of rapid AI advances has created more opportunities than ever for anyone — including those just entering the field — to build software. In fact, many companies just can’t find enough skilled AI talent. Every winter holiday, I spend some time learning and building, and I hope you will too. This helps me sharpen old skills and learn new ones, and it can help you grow your career in tech. To be skilled at building AI systems, I recommend that you: - Take AI courses - Practice building AI systems - (Optionally) read research papers Let me share why each of these is important. I’ve heard some developers advise others to just plunge into building things without worrying about learning. This is bad advice! Unless you’re already surrounded by a community of experienced AI developers, plunging into building without understanding the foundations of AI means you’ll risk reinventing the wheel or — more likely — reinventing the wheel badly! For example, during interviews with job candidates, I have spoken with developers who reinvented standard RAG document chunking strategies, duplicated existing evaluation techniques for Agentic AI, or ended up with messy LLM context management code. If they had taken a couple of relevant courses, they would have better understood the building blocks that already exist. They could still rebuild these blocks from scratch if they wished, or perhaps even invent something superior to existing solutions, but they would have avoided weeks of unnecessary work. So structured learning is important! Moreover, I find taking courses really fun. Rather than watching Netflix, I prefer watching a course by a knowledgeable AI instructor any day! At the same time, taking courses alone isn’t enough. There are many lessons that you’ll gain only from hands-on practice. Learning the theory behind how an airplane works is very important to becoming a pilot, but no one has ever learned to be a pilot just by taking courses. At some point, jumping into the pilot's seat is critical! The good news is that by learning to use highly agentic coders, the process of building is the easiest it has ever been. And learning about AI building blocks might inspire you with new ideas for things to build. If I’m not feeling inspired about what projects to work on, I will usually either take courses or read research papers, and after doing this for a while, I always end up with many new ideas. Moreover, I find building really fun, and I hope you will too! [Truncated for length. Full text: https://lnkd.in/gwv8nsgN ]

In the last 4 months, I've tried and tested 7 coding AI Agents Here are my top 5, and here's when to use them as well... Coding agents cannot be ignored now. These agents are not only moving markets but are the core foundation of AI-Native companies in 2026. And in the last 4 months, I tested major coding agents, from open source options like Open Code, Cline, to paid options like Cursor. 📌 Let me break down all 5 coding agents so you get to when to use which one: 1\ OpenAI Codex - Cloud-based coding agent that runs tasks in isolated sandboxes via CLI - Best for: Background/async tasks, parallel agents, CI/CD pipelines - Use when: You need to automate large-scale coding tasks without touching the IDE Quick Start Guide: https://lnkd.in/gKUgFnPH 2\ Claude Code - Anthropic's terminal-based agentic coding tool — works directly in your codebase - Best for: Large refactors, multi-file edits, complex debugging - Use when: You live in the terminal and need deep, repo-level reasoning Quick Start Guide: https://lnkd.in/gSAYPN4b 3\ GitHub Copilot - AI pair programmer embedded across VS Code and the entire GitHub ecosystem - Best for: Inline autocomplete, quick snippets, PR reviews - Use when: You want frictionless suggestions without changing your existing workflow Quick Start Guide:https://lnkd.in/gdmERrn5 4\ Cursor - AI-native code editor (fork of VS Code) with deep codebase understanding - Best for: Complex cross-platform testing, faster edits across multiple files - Use when: You want an AI-first editor that understands your full project context Quick Start Guide: https://lnkd.in/gf8jbtdv 5\ Antigravity - Google's autonomous code editor — agent-first IDE (fork of VS Code) powered by Gemini 3 - Best for: End-to-end task execution, native Google model APIs, browser-based testing - Use when: You want to act as an architect and delegate full tasks to autonomous agents Quick Start Guide: https://lnkd.in/gQXQbHHk 📌 Quick decision guide: 1\ Need async, sandboxed task automation → Codex  2\ Terminal-first, large codebase refactoring → Claude Code  3\ Daily autocomplete within VS Code/GitHub → Copilot  4\ AI-native editor with deep project context → Cursor  5\ Orchestrate multiple agents end-to-end → Antigravity If you want to understand AI agent concepts deeper, my free newsletter breaks down everything you need to know: https://lnkd.in/g5-QgaX4 Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents

I recently sat down with Erran Berger, VP of Product Engineering at LinkedIn, to discuss a question that’s on every developer’s mind: How is AI actually changing the way we build software? We’re moving past the "AI will write all the code" hype and into a much more interesting reality. The role of the software engineer isn't disappearing; it’s being elevated. 🤌 TL;DR from the conversation: 1/ Systems Thinking > Syntax: As AI handles more of the boilerplate, the value of an engineer shifts toward orchestration and high-level architecture. 2/ The "Human Editor": AI can generate solutions, but human judgment remains the final (and most critical) line of defense for security, ethics, and performance. 3/ Solving Technical Debt: One of the most exciting use cases Erran shared was using AI to refactor legacy systems—turning a months-long headache into a manageable project. 4/ New Must-Have Skills: If you aren't already looking into RAG, LLMOps, and Vector Databases, now is the time to start. The goal isn't just to write code faster; it's to make engineering "joyful" again by removing the friction and focusing on pure problem-solving. Watch the full episode here: https://lnkd.in/gEJb4jdz Thank you, LinkedIn team for inviting me over, for this incredibly insightful conversation 🫶

I was convinced AI would transform how we build software. I did not expect it to happen so fast. Over the past year, through conversations with leaders like Thomas Dohmke, startups in the AI software development space, working with the Anthropic team, and observing our own builders at Doctolib, one thing has become clear to me. AI is changing how we think about building software like nothing before. Specs turn into working prototypes instantly. Design systems and architecture principles are continuously reinforced by the tooling itself. Writing production-ready code from scratch is no longer our bottleneck. Tests are generated automatically to validate intent. Complex refactoring is handled by autonomous agents. And this is accelerating. As Ethan Mollick once said: "The AI we use today is the worst AI we will ever use.” Better models enable more capable agent fleets and higher autonomy, which in turn drive even better models As tech builders, our day-to-day job is changing… We don’t focus as much on manual implementation, writing boilerplate, or debugging line by line. Instead, we design the systems and scaffolding that allow AI to do reliable work. We orchestrate agents with the right intents, we validate AI-generated architectures, and we define strict quality guardrails. ….but the outcome doesn’t change: creating better technologies for our users. This is a strong opportunity for all tech companies to innovate faster, but for us even more so in view of the specificities of healthcare and the quality of our technologies and teams. 🔹 AI will help us create more value for our health professionals and anyone managing their health. 🔹 AI will help us tackle all user feedback, bugs and incidents in minutes. 🔹 AI will make us launch more specialties and more countries faster. At Doctolib, we're going all-in on this transformation. Dozens of specialized agents deployed. Our engineering leaders are driving this change, committing code 5x more frequently than a year ago. Teams already deliver significantly more value to patients and health professionals. If you want to join that revolution and contribute to reinventing the daily life of health professionals and improving health for everyone, we welcome all builders. It's only the beginning.

Everyone keeps asking me the same question: “Is my Salesforce Developer job at risk because of AI?” Let me make this simple: Your job isn’t at risk. But your skill set might be. AI isn’t coming for Salesforce developers. AI is coming for developers who still write code the same way they did in 2018. Here’s the truth nobody wants to say: The developers who learn AI → will replace the developers who don’t. And Salesforce is moving faster than ever: Agentforce. Prompt Builder. Data 360. AI-powered development environments. Automations that write half your boilerplate code for you. This isn’t the end of Salesforce development. This is the biggest opportunity we’ve had in a decade. New Roles. New Skills. New Money. • AI-enhanced automation designers • Prompt + Agent builders • Data Cloud + AI orchestration specialists • Integration developers who use AI to deliver 5x faster • Devs who can blend Apex, metadata, and intelligence into real business outcomes So… Will developer demand drop? Absolutely not. Companies don’t want fewer developers. They want developers who can ship faster, smarter, and more intelligently — and AI is the amplifier. If you evolve, you’ll be more in demand. If you ignore AI, you’ll be… well, replaceable. The future is hybrid: You + AI. Learn it. Leverage it. Lead with it.