How Markdown Specs Boost Software Development

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

Every developer has faced the late-night debugging dread, spending hours unraveling spaghetti logic that poor planning could’ve prevented. In our latest blog, we share a practical, brain-friendly framework to save you from those sleepless, panic-fueled sessions. Here’s a snapshot of the principle behind it: ➔ 15 minutes of smart planning = 5 hours saved from debugging ➔ Research shows our brains process visuals 60,000x faster than text. That’s why mind maps, flow diagrams, and quick sketches shift you from reactive coding to proactive designing. ➔ Visual reasoning = senior-level problem solving Visual diagrams help you spot system-wide dependencies, corner cases, and user pain points before you write a single line of code. ➔ The 15-Minute Framework – Designed to align with how developers think: ➔ Sketch the feature flow or states (loading, success, error) ➔ Ask “What could go wrong?” ➔ Plan minimal safeguards or recovery flows ➔ Code with clarity and fewer surprises Want to see this planning framework in action? Read the full blog here: https://lnkd.in/gZsr8BMk At Wow Labz, effective planning isn’t just a nice-to-have. It’s the foundation for building resilient AI agents, robust feature flows, and scalable digital products. If you're ready to build better, smarter systems—let’s connect: https://lnkd.in/gY37rtBW We’re ready to help you plan, build, and ship AI-powered workflows today. #DeveloperProductivity #PlanningFramework #MindMapping #AIWorkflow #WowLabz

Just wrapped up another "quick fix" that turned into a 3-day rabbit hole 🤦♂️ Here's what I've learned after 8 years in tech: In 2025, solving technical challenges isn't the hard part anymore. We have AI, Stack Overflow, and frameworks that practically code themselves. The real challenge? Asking the right questions before you write a single line of code: → What business problem are we actually solving? → Will this solution still make sense in 18 months? → Are we building for today's requirements or tomorrow's scale? → How does this fit into our broader product strategy? I've seen too many "elegant" solutions that worked perfectly... for exactly 6 months. Then came the feature requests, the scale issues, the integration nightmares. The difference between a good developer and a great one isn't technical prowess—it's the discipline to step back and think about the why before diving into the how. Sometimes the best code is the code you don't write. What's your take? Are we solving the right problems, or just solving problems the right way? #ProductDevelopment #TechStrategy #SoftwareEngineering

🚨 The fastest way to kill a promising software product? Skip system design. 🚨 I once watched a team dive straight into coding without architecture, no plan for rate limiting or scaling when more users were acquired, they were just happy to hash out this clever new idea they had. Six months later, adding a small feature took weeks, bugs kept resurfacing, costs exploded, the code broke too many times to count, and morale collapsed. All because the foundation was ignored. System architecture isn’t paperwork. It’s prevention. It’s what makes your product scalable, maintainable, and future-proof. And with AI code assistants generating code faster than ever, structure matters even more. AI can write functions, but it won’t save you from spaghetti systems, hidden dependencies, or crushing technical debt. Skipping design feels fast in the moment. But you’ll always pay for it later — in time, money, and frustration. Great software isn’t just built. It’s architected , designed. 👉 Has skipping architecture ever come back to bite your team? #SystemDesign #SoftwareArchitecture #TechLeadership #AI

"Building software has never been easier. Building good software has never been harder." Paradox of the AI age: Anyone can create an app in a weekend but the failure rate has never been higher The bottleneck isn't development anymore. It's decision-making. - Which features matter? - How do you prioritise the backlog? - What does "done" actually look like? - How do you measure success? I see brilliant technical founders who can architect beautiful systems but struggle to architect user journeys. They optimise for code elegance instead of user delight. Product Management comes alongside our development at PostMVP. It's not about feature factories. It's about building products that users actually want to pay for. 📣 The technical bar is lowering. The product bar is rising. #ProductManagement #AI

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.

This is my exact process for building MVPs (in 60 sec) When you hear “build an MVP,” you probably imagine months of coding, endless features, and thousands of dollars. But in truth, the process can be broken into minutes—not months. Here’s what I everyone do (the hard way): - Spend weeks drafting business plans. - Hire developers before having users. - Add every feature I could think of. - Delay launching until it was “perfect.” - Burn money before learning anything. Here’s what I do now with AI Possibilities (the simple way): - Start with one clear problem to solve. - Sketch the idea on paper. - Build a wireframe. - Use Lovable or Replit for the first version. - Show it to 5 users immediately. The difference is staggering. One process is rooted in pride and fear. The other is rooted in clarity and speed. And only one actually creates traction. Simple beats complex when building MVPs. PS: If you had to launch in 60 seconds, where would you start?

This is a perfect moment to show, not just tell. Today I’m sharing a full Overcut demo for the first time - so exciting!!! 🚀 In this short demo, you’ll see a complete flow: ✅ A developer creates a new ticket ✅ Overcut automatically triages it ✅ An AI agent drafts the design ✅ A pull request is created ✅ Overcut runs a code review on the PR ✅ And even responds to developer comments by implementing the requested changes This isn’t a vision slide. It’s working 🤩 👉 You’re seeing the future here: Agentic workflows running inside the SDLC, not just sitting in your editor. Curious, which part of this flow would make the biggest impact for your team?

Every mistake building ModBaseAI has taught me one thing: Founders obsess over tech. But they ignore clarity. I’ve been guilty of it too. Weeks spent polishing features, dashboards, integrations... Only to realize the real problem wasn’t the code. It was this: 👉 Do customers get the value? Because tech isn’t the bottleneck. Clarity is. Clarity in: • Who the product is for. • What problem it actually solves. • How customers describe that problem in their own words. Without clarity → even a flawless product feels like noise. With clarity → even a scrappy MVP feels like magic. The mistakes sting, but the lesson is simple: 💡 Code can always be fixed. Lack of clarity will kill you first. What’s harder in your experience — building the tech, or making the value unmistakably clear?

Vibe coding is delivering impressive results for code generation with today’s LLMs. But enterprise systems aren’t built from ad‑hoc prompts alone. In practice, trying to build a full system with prompts can mean hundreds—or thousands—of micro‑instructions. That often leads to “fix this” loops, spiraling token costs, and, ultimately, prompt fatigue. Many teams end up reverting to proven engineering discipline for stability and scale. We also shouldn’t forget the SDLC. It’s taken decades to mature how we capture requirements, design systems, validate security, and manage change. Code generation is not the same as system requirements. Quality is the degree to which a system meets user needs—so we still have to elicit and engineer those needs, design the architecture, and then use vibe‑coding tools inside that framework. The good news: there’s an AI‑augmented system design option—AutoSAD—that applies LLMs within proven SDLC approaches. Use AutoSAD to reduce prompt fatigue and token burn—and avoid the “fix this” loop—while keeping the rigor enterprises need. #Innovation #Management #DigitalMarketing #Technology #Creativity #Future #Marketing #SocialMedia #Startups #LLM #AUTOSAD