Week 1: Validate the Idea

  • Talk to 20 potential users. Understand their pain points.
  • Research competitors. Find gaps in existing solutions.
  • Define your unique value proposition. What can you do 10x better?
  • Pick your tech stack: Keep it simple — Next.js + Vercel AI SDK + one LLM API.

Week 2: Build the Core Feature

  • Focus on ONE core feature that delivers value.
  • Use existing APIs (OpenAI, Anthropic) instead of training models.
  • Build a minimal UI — function over form.
  • Set up basic auth and usage tracking.

Week 3: Add Polish

  • Improve error handling and edge cases.
  • Add loading states and streaming responses.
  • Implement rate limiting to control costs.
  • Write clear onboarding copy.

Week 4: Launch and Learn

  • Share on Twitter/X, Hacker News, and relevant communities.
  • Set up a feedback loop (form, Discord, or email).
  • Track key metrics: activation rate, retention, NPS.
  • Iterate based on user feedback, not assumptions.

Common Mistakes

  • Building for months before talking to users.
  • Over-engineering the architecture from day one.
  • Ignoring costs until the bill arrives.
  • Competing directly with OpenAI/Google on general-purpose AI.

The 5-Day MVP Blueprint

Day 1–2: Define the core user problem in one sentence. Build a prompt pipeline that takes user input and returns a useful AI-generated output. Don’t worry about UI yet—test via API or a simple CLI.

Day 3: Wrap it in a minimal web interface. Use Next.js or Streamlit. Add authentication with Clerk or NextAuth. Deploy to Vercel or Railway.

Day 4: Add one “wow” feature—something that makes users say “I didn’t expect that.” This could be personalized recommendations, multi-modal input (upload an image and get analysis), or real-time collaboration.

Day 5: Get 10 real users. Not friends—strangers. Post in relevant subreddits, Discord servers, or Twitter. Collect feedback obsessively.

Cost Optimization from Day 1

  • Use GPT-5-mini or Claude Haiku for 80% of tasks; reserve GPT-5 or Claude Sonnet for complex reasoning.
  • Cache identical or similar requests. A semantic cache (e.g., using Redis + embeddings) can cut API costs by 40–60%.
  • Set hard spending alerts. Most cloud AI APIs let you set budget caps. Start with $50/day and adjust based on user growth.
  • Batch where possible. If your feature doesn’t require real-time responses, batch requests during off-peak hours for cheaper rates.