Aakash GuptaComplete Vibe Coding Tutorial: Build a Full Stack App with AI | Andy Carroll (Windsurf)
CHAPTERS
Defining “vibe coding”: humans + AI building together
Aakash and Andy open by clarifying what “vibe coding” actually means: using AI development tools as a creative partner to rapidly bring products to life. They also highlight an important product lesson—start by defining what makes the idea unique and focus energy there.
Choosing a tool: why Andy prefers Windsurf (vs. v0, Lovable, Replit, Cursor)
Andy compares popular AI dev tools and explains where each fits in a builder’s journey. He positions Windsurf as a more powerful, full-stack-friendly editor with broad model access, after hitting limits with simpler “quick-start” tools.
First look at Windsurf’s workspace: projects, files, and Cascade
Andy screen-shares and demystifies Windsurf’s UI: a file tree on the left, code in the middle, and the Cascade chat panel on the right. He emphasizes that it can look intimidating at first, but is essentially folders + an AI editor.
Cascade modes that matter: Chat vs. Write (and why it can break things)
Andy explains the crucial difference between Chat mode (safe, no edits) and Write mode (AI can directly change code). He warns that AI can be overzealous—small requests can trigger large refactors—so controlling permissions is essential.
Model selection strategy: using different LLMs for different tasks
They discuss how different models perform better depending on the job—broad thinking vs. tactical debugging. Andy shares a practical workflow: switch models when stuck, and leverage “thinking” variants to explore tradeoffs and context.
Planning before building: prompt libraries, product docs, and avoiding false starts
Andy describes an early mistake: jumping straight into building without enough planning led to expensive rework. He now uses a structured prompt library and lightweight product artifacts (strategy, architecture, roadmap) to clarify direction before coding.
Project case study: the AI sports match reporter idea and early prototype
Andy shares the origin story: a kids’ football match report sparked an idea for an AI match reporter that generates written and audio outputs. He shows how he’s iterating from a simple waitlist/landing page toward a fuller multi-app product.
From PM artifacts to a single cockpit: roadmap + GitHub as the shared source of truth
Andy shows how he stores strategy docs, prompts, and roadmaps inside the repo so the whole project context lives alongside the code. He highlights how this makes status reporting and alignment dramatically faster for teams and stakeholders.
Deployment lessons: why “deploy early” matters (Netlify/Vercel)
Andy contrasts one-click deployments in simpler tools with the more complex reality of full-stack setups. His key lesson: deploy the simplest working version early and often to avoid painful late-stage deployment failures.
Live edit demo: fixing landing page contrast with screenshot + Write mode
They perform a real-time improvement: Andy uses a screenshot, prompts the model, and updates the “Coming Soon” text color for readability. This segment reinforces how quickly AI can turn UI feedback into code changes and deployments.
Local dev vs. production: running localhost and using the terminal inside the IDE
Andy demonstrates spinning up the site locally (localhost) and notes how Windsurf reduces tool-switching by integrating terminal commands. They compare seeing changes immediately locally vs. waiting for production deploys.
Shipping more features live: logo + favicon + “How it works” infographic section
They generate a basic brand logo and favicon via a prompt, then implement them directly into the site. Next, they add an interactive “How it works” section, showing how AI can quickly create polished, hoverable UI components from a simple prompt.
Big-picture takeaways: creativity, team alignment, and monetization paths
Andy argues AI dev tools increase creativity by collapsing the cost of experimentation and feedback. They discuss monetization via micro-SaaS, rapid trials (Peter Levels), and consulting/enablement as another pathway.
Implications for PMs and Andy’s career journey: from 2008 to AI-driven building
They reflect on how product management has evolved and why this shift feels bigger than prior movements like Agile. Andy shares his path from finance to product, his current work at Aisle Partners (training + implementation), and the realities of going independent.
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