The problem: Most people open an AI tool and type "build me a landing page." They get something generic, spend an hour fixing it, and still aren't happy. The fix: Write the spec first. 3 minutes of clarity saves 3 hours of iteration. Before you touch any AI tool, answer these questions in a doc: - Who is this page for? - What's the ONE thing you want them to do? - What are the 3 reasons they should do it? - What objections might stop them? That's your spec. Now feed it to your coding agent. Instead of "build me a landing page," you're saying "build a landing page for indie hackers who want to ship faster, with a hero section, 3 benefit blocks, and a single email CTA above the fold." Night and day difference in output. The AI stops guessing. You stop fixing. The page ships. Try it on your next build and drop your result below 👇 Save this for your next build 📖

You need to be able to describe what you want to build. Here are 5 SaaS products non-technical founders have shipped using spec coding: - Client portal - share files, feedback, and invoices with clients. No more "can you resend that?" emails. - Digital product store - sell your templates, guides, or courses. Your own Gumroad, your own rules. - Booking tool - let customers schedule calls or sessions. Calendly, but branded and yours. - Feedback collector - embed a form, capture responses, get a dashboard. Typeform without the monthly bill. - AI chatbot for your business - trained on your docs, embedded on your site, answering FAQs 24/7. None of these required writing a single line of code manually. The workflow: Write the spec → Break it into stories → Let SAM build it → Review the PR → Ship. If you can write a Google Doc, you can spec code a SaaS. What would YOU build if implementation wasn't the bottleneck? 👇

https://medium.com/agentic-spec-coding/from-prd-to-working-app-in-11-minutes-zero-human-intervention-758cfaea48dc

How Vibe Coding Changed Everything (And Why Developers Are Moving Beyond It) In February 2025, Andrej Karpathy, co-founder of OpenAI and former AI leader at Tesla, introduced the term "vibe coding" — and software development hasn't been the same since. But vibe coding didn't appear overnight. It was the culmination of decades of evolution in how we interact with code, and it's already transforming into something new: spec coding. Here's the full story. ---------------------------- The Long Road to Vibe Coding The WYSIWYG Era (2003-2013) The seeds were planted long before LLMs existed. Consumer website builders like Squarespace (2003/04), Wix (2006), Weebly (2006), and later Webflow (2013) established expectations for instant feedback, layout control, and "describe then tweak" workflows that would later resonate with AI-assisted development. These tools made non-programmers feel like they could create software. The promise: skip the code, focus on the outcome. The API Moment (2020-2024) Everything changed when AI became accessible. OpenAI's API launched in 2020, normalizing the pattern of "call a model, get code/text" — the foundation of modern AI coding tools. Tools like GitHub Copilot (2021) started as "glorified typers" — advanced autocomplete that finished your code as you typed. Then came agentic chat tools like Cursor, which could analyze entire codebases and hold multi-turn conversations about your code. But these were still assistants. You were still writing most of the code. The Vibe Coding Breakthrough (February 2025) Then Karpathy dropped his post. He described vibe coding as "fully giving in to the vibes, embracing exponentials, and forgetting that the code even exists" — a fundamental shift where developers provide natural language descriptions and let AI generate complete, working code. Instead of translating ideas into the rigid syntax of programming languages, developers could now just talk, describe what they wanted, and let AI handle the rest.

You've heard about spec coding. Maybe you saw someone ship a feature in an hour that would've taken you a week. Maybe you're drowning in backlog and can't keep up. Here's what you need to know to get started. What Spec Coding Actually Is: Stop writing code line by line. Start writing specifications. The workflow: 1. You define WHAT you want (the spec) 2. AI figures out HOW to build it (the code) You become the architect. AI becomes the builder. Why This Works The old way: - You: Think about the feature - You: Write the code - You: Write tests - You: Debug - You: Review - You: Ship The spec coding way: - You: Think about the feature (write a clear spec) - AI: Writes the code, tests, and handles implementation - You: Review and merge You do the high-value thinking. AI does the repetitive implementation. The Three Core Documents 1. PRD (Product Requirements Document) Defines what you're building and why. Must include: - Problem you're solving - Who it's for - What success looks like - What you're NOT building Example: Problem: Users can't reset their password Target: Existing users who forgot their login Must-have: Email-based reset flow Success: User can reset password in under 2 minutes Out of scope: SMS reset, social login recovery 2. User Stories Break features into specific, testable pieces. Format: "As a [user type], I want to [action] so that [benefit]" Example: "As a logged-out user, I want to request a password reset email so that I can regain access to my account" 3. Acceptance Criteria How you know the story is done. Format: Given [context], when [action], then [result] Example: - Given I'm on the login page - When I click "Forgot Password" and enter my email - Then I receive a reset link within 5 minutes Your First Spec Coding Project Pick something small. A single feature or component. Not your entire app. Good first projects: - Contact form with validation - User profile page - Simple API endpoint - Dashboard component