How to save 5 hours of debugging with 15 minutes of planning

🚨 The Hidden Cost of Building Without Specs Ever spent weeks building a feature, only to hear: “That’s not what we wanted”? Painful, right? There’s a fix that’s quietly transforming how high-performing teams ship software. 📝 Markdown Specs: Small Habit, Big Impact Write detailed Markdown spec before you code (vibe code, AI augment etc). The results are eye-opening: ⚡ 65% faster delivery 🔄 78% less rework 🎯 89% fewer surprise requirements ✅ 94% test coverage generated directly from specs 🚀 The Real Shift This isn’t red tape, it’s acceleration. Tools like OpenAI’s Model Spec prove we’re moving from “hoping we built the right thing” → to “knowing we did.” 💡 Curious: Have you tried specification-driven development (SDD) with AI yet? Full article linked. #SoftwareEngineering #AIinDevelopment #TechnicalLeadership #DevOps #AgileTransformation #DeveloperProductivity

From zero to my first Engineering Productivity Metrics Analyser with Claude. What started as a simple question about engineering productivity metrics analysis turned into a fully automated solution in under two hours. My Claude Code experience: - Started with a plain English description of what I wanted - Claude built a complete analyser, RAG status classification, and automated reporting - When I mentioned JavaScript would be easier to run - instant conversion, no hassle - When I asked for parameterization for future data sources - boom, CLI arguments, auto-detection, batch processing What impressed me most: - Zero setup friction - went from idea to working solution instantly, otherwise it will take months of development time - Iterative improvement felt natural - like pair programming with an experienced engineer - Well- designed code, help documentation, and build in flexibility The Result: An analyser that automatically: - Classifies RAG levels  - Detects trends and patterns - Handles multiple data sources seamlessly 💡 This is what the future of development looks like - focusing on WHAT you want to build rather than HOW to build it. #ClaudeCode #AI #VibeCoding #EngineeringProductivity #DeveloperExperience #Innovation

From PM Idea to Production-Ready Tool: Complete Vibe Coding Series Just launched a 3-part series showing PMs how to build AI-powered tools or prototypes to demonstrate your ideas without waiting for dev resources. Here's what I created and shown viewers to build from scratch: Video 1 - https://lnkd.in/g-Eig9d8 Vibe Coding Basics: Built a hypothesis generator that turns product problems into structured, testable hypotheses in seconds. No more starting from blank pages when analyzing user drop-offs or feature adoption issues. Video 2 - https://lnkd.in/gaYHXHhb Database Integration & AI Insights: Extended it into a complete feedback collection system with Supabase database, automatic AI categorization, and export capabilities. Real feedback gets instantly sorted into actionable categories with summaries. Video 3 - https://lnkd.in/g7kgUDwm Authentication & Personalization: Added Google OAuth, role-based dashboards, and personal scope filtering. Same tool now serves both exec-level metrics and PM-detailed analysis based on who's logged in. The transformation: 40 minutes from basic form to enterprise-grade PM intelligence platform. Key insights: PMs who can prototype their own solutions or own ideas move from "let me check on that" to "here's how it looks as a demo" in stakeholder meetings. Created in collaboration with Shailesh Sharma #VibeCoding #ProductManagement #AIforPMs #Prototyping #NoCode #PMTools #Authentication #DatabaseIntegration

Getting specifications right has always been critical for building good software. The research equivalent is asking the right questions. Too often, though, engineers and companies, eager to ship fast under the banner of “rapid iteration”, skip over requirements and specifications. Great for demos, not so great for business. Enter #SpecKit, an open-source toolkit from GitHub. Think of it as specification-driven development (SDD), similar to test-driven development. It’s easy to set up, with a few commands that guide you through the deliberate process: clarify specs, create a plan, and then implement. Why do I think this matters? Historically, developers got rapid feedback on syntax (will it compile?) and semantics (do the tests pass?). Test-driven development bridged specs and implementation, where we literally wrote tests before code. Automation and now GPT-based tooling have accelerated this. But there was always a gap: natural-language specs vs. machine-readable code. We never encountered the equivalent of a “your specs didn’t compile” error, nor did we have strong guarantees that the specs accurately describe the code and that the code implements the specs. Aligning specs has remained a slow, high-friction, collaborative process (shout out to Oren Toledano and the folks at Swimm for their innovative work advancing tooling on that front). With tools like SpecKit, we’re moving toward a future where specifications become the dynamic artifact of record. Per the blog post from GitHub, “AI makes specifications executable.” Code is becoming the commodity piece; intent, captured in specs, is the source of truth. Worth remembering that AI can write code well when the specs are good, but it can’t read your mind. You wouldn’t tell a teammate “just build something transformative” and expect success. The same holds for AI. So whether you’re prototyping or scaling infrastructure, start with the specs. As agents and automation multiply our leverage, the cycles that matter most are spec-first + verify. If something breaks, check your specs first.

Found another tool that helps sustain Claude Code / Cursor context. It's called ByteRover. Every time you start a new coding session with Cursor or Claude, you're back to square one. Explaining your coding patterns, architectural decisions, and project quirks all over again. ByteRover creates a persistent memory layer that your AI agents actually remember between sessions. The system captures every successful solution, debugging fix, and implementation pattern into searchable memories. When your AI encounters similar contexts, it automatically retrieves relevant knowledge. No more explaining that you prefer specific error handling patterns or that your API follows certain conventions. What makes this powerful for teams is memory workspace sharing. Your entire team's collective knowledge becomes accessible to every AI agent. New developers inherit institutional knowledge instantly. That tricky authentication flow someone solved last month? It's already in the memory layer. The technical implementation uses MCP (Model Context Protocol) to integrate seamlessly with Cursor, Windsurf, Cline, VS Code, and Zed. Memories auto-save after successful tasks and auto-recall when relevant context appears. You can bookmark critical memories, add comments for clarity, and organize by project workspace. Most interesting feature: version history and rollback for memories. You can manage context evolution just like you manage code, merging improvements and reverting problematic changes. https://lnkd.in/edeTz7Ju

After testing three major AI development tools on complex personal projects, here’s my comparison: 🚀 Augment Code vs Cursor vs Kiro - The Reality Check 📑 Documentation-first methodology proved crucial: • requirements.md — user stories & acceptance criteria • design.md — component architecture & implementation details • tasks.md — phased implementation roadmap (45 detailed tasks) ⚙️ Execution discipline: Implement → fix compile errors → verify functionality → commit → next task. This rhythm kept focus and ensured consistent progress. 🔍 Where the differences emerged: Augment Code → surgical precision in debugging. It analyzed issues, created focused remediation plans, and touched only the files with actual problems. Most importantly, it preserved context across multi-file enterprise architectures. Cursor → produced monolithic code, often over-edited during debugging (sometimes deleting unrelated lines). Refactoring structured codebases was difficult. Kiro → started strong with architectural planning, but over time struggled to maintain stability. Complex integrations (e.g., Monaco Editor diff visualization) remained unresolved. 💡 Unexpected advantage: Staying in my familiar WebStorm environment eliminated context switching and preserved productivity flow. Bottom line: For enterprise-grade development that demands sustained context awareness and precise debugging, Augment Code delivered where others struggled with complexity inheritance and technical debt resolution. 👉 What’s your experience with AI dev tools on complex, real-world projects? 💭 #AI #Development #SoftwareEngineering #Enterprise #Productivity

Unpopular opinion: You're probably using Claude Code wrong (and it's costing you hours) ⏰💸 I see developers every day treating Claude Code like a fancy autocomplete tool. Big mistake. 🚫 After diving deep into Claude Code's advanced workflows, I discovered most devs are missing 7 game-changing features that could 10x their productivity: 🧠 Plan Mode → Explore codebases safely without making changes 🔍 Extended Thinking → Get deeper analysis for complex problems 📚 Smart Codebase Understanding → Grasp new projects in minutes, not hours 🎯 Intelligent Code Finding → Locate any functionality instantly 🐛 Efficient Bug Fixing → Debug with AI-powered precision ⚡ Code Refactoring → Modernize legacy code automatically 🧪 Test Generation → Create comprehensive test suites effortlessly The difference? ❌ Basic users: "Hey Claude, fix this bug" ✅ Advanced users: Leverage specialized workflows that understand context, plan strategically, and deliver targeted solutions Most developers are stuck in the "chat with AI" mindset when they should be thinking "AI-powered development workflows." That's why I created this comprehensive presentation 📊 → 10 slides covering all 7 workflows → Real commands and examples from Anthropic's docs → Step-by-step implementation guides → Terminal-style design (because we're developers 😎) → Best practices I wish I knew months ago What's your biggest Claude Code challenge? Drop a comment below - I'd love to help you unlock these workflows! 👇 #ClaudeCode #AI #SoftwareDevelopment #Productivity #DevTools #Coding

I spent years in my "red suit" - writing thousands of lines of code, debugging for hours, building everything from scratch. Then I discovered the "black suit" - automation + AI. Just like Spider-Man leaves behind his old suit to embrace a new form, people in tech are increasingly leaving behind manual coding-heavy approaches and adopting no-code/low-code automation platforms combined with AI tools. Before (Programming): - 2 weeks to build a data pipeline - 500+ lines of custom code - Constant maintenance headaches - Single point of failure (me) After (Automation + AI): - 2 hours with n8n + coding AI agents - Visual workflows anyone can understand - Instant debugging process - Non-technical team members can modify without coding The truth is: The best developers in 2025 are orchestrators, not coders. They connect APIs instead of building them. They configure AI agents instead of writing algorithms. They design workflows instead of debugging loops. Your IDE might judge you, but your productivity won't. Over to you: Which Spider-Man are you right now? 🕷️