developer coding user risk: low
Solo Founder 14-Day Launch System Builder
Instructs building a web app called 'Zero to One', a 14-day structured system for solo founders from idea to first paying customer, featuring idea intake with validation, personali…
PROMPT
Build a solo-founder launch system called "Zero to One" — a structured 14-day system for going from idea to first paying customer.
Core features:
- Idea intake: user inputs their idea, target customer, and intended price point. [LLM API] validates the inputs by asking 3 clarifying questions — forces specificity before any templates are generated
- Personalized playbook: 14-day calendar where each day has a specific task, a customized template, and a success metric. All templates are generated by [LLM API] using the user's specific idea and customer — not generic. Day 1: problem validation script. Day 3: landing page copy. Day 5: outreach email. Day 7: customer interview guide. Day 10: sales conversation framework. Day 14: post-mortem template
- Daily execution log: each day the user marks the task complete and answers: "What happened?" and "What's the specific blocker if incomplete?" — two fields, 150 chars each
- Decision tree: if-then guidance for the 8 most common sticking points ("No one responded to my outreach → here are 3 likely reasons and the fix for each"). Structured as interactive branching, not a wall of text
- Launch readiness score: composite of daily completions, outreach sent, and conversations held — shown as a 0–100 score that updates daily
- Post-mortem: on day 14, guided reflection template — what worked, what failed, what the next 14 days should focus on. AI generates a one-page summary
Stack: React, [LLM API] for all template generation and decision tree content, localStorage. High-energy design — daily progress always front and center. EXPECTED OUTPUT
- Format
- code
SUCCESS CRITERIA
- Implement idea intake with LLM API validation via 3 clarifying questions
- Implement personalized 14-day playbook with customized templates and success metrics for specified days
- Implement daily execution log with completion mark and two 150-char fields
- Implement interactive branching decision tree for 8 common sticking points
- Implement launch readiness score as 0-100 composite updating daily
- Implement day 14 post-mortem with guided template and AI-generated one-page summary
- Use React, LLM API for templates and decision tree, localStorage stack
- Apply high-energy design with daily progress front and center
FAILURE MODES
- Generating generic templates instead of personalized ones
- Structuring decision tree as a wall of text instead of interactive branching
- Skipping input validation or specificity forcing
- Omitting key days or tasks in the playbook
- Failing to implement daily score updates
- Using incorrect tech stack or storage
- Neglecting high-energy design elements
CAVEATS
- Missing context
-
- Full list of tasks for all 14 days.
- UI wireframes, mockups, or detailed design specs for 'high-energy design'.
- Secure handling of LLM API keys (localStorage may not suffice).
- Deployment instructions or hosting requirements.
- Exact computation formula for 'launch readiness score'.
- Ambiguities
-
- Does not specify tasks for all 14 days, only samples like Day 1,3,5,7,10,14.
- '[LLM API]' is a placeholder without specifying the provider or model.
- 'High-energy design' is vague without style guidelines or examples.
- Decision tree mentions 8 sticking points but only gives one example.
QUALITY
- OVERALL
- 0.70
- CLARITY
- 0.85
- SPECIFICITY
- 0.80
- REUSABILITY
- 0.25
- COMPLETENESS
- 0.75
IMPROVEMENT SUGGESTIONS
- Explicitly list tasks and templates for all 14 days.
- Replace '[LLM API]' with a specific provider (e.g., OpenAI GPT-4o) and include API integration details.
- Provide a style guide or color palette for 'high-energy design'.
- Detail the 8 common sticking points and their if-then branches.
- Specify output deliverables, e.g., 'GitHub repo with deployed demo'.
USAGE
Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.
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