Most founders waste months perfecting features users don't want. I took a different approach.
Three weeks ago, I had nothing – no POC, no MVP, not even a sketch. Today, I'm sending beta invites for a tool that lets users test prompts across multiple AI models simultaneously to find the best quality and value.
Full disclosure: I'm a product guy, not a developer. My "production" system runs on Replit with file-based storage instead of a proper database. But it works, and that's what matters for validation.
Here's how I accelerated from zero to beta:
1. Claude Code Changed Everything
I resisted CLI coding tools for months, preferring the control of Claude's web UI. Copy-pasting code felt safer, more deliberate.
That approach crumbled as my codebase grew. Copy-paste errors multiplied. Progress slowed to a crawl.
Claude Code solved this instantly. The CLI integration gives it full codebase context, while Anthropic's coding capabilities shine through direct implementation. I combined this with product requirement docs created in Claude's web UI, stored in my repo for Claude Code to reference.
The result? Seamless transitions from strategy to execution.
2. I Built an AI Team, Not an AI Assistant
Here's my breakthrough: I don't want one AI assistant. I want a whole team.
My setup includes:
- Maya (COO): Main organizing agent who analyzes requests
- Specialized agents: CTO, product lead, growth marketer, legal advisor
- Project documentation: Positioning, roadmap, and specialist prompts in Google Docs
- Integrated GitHub repo for context
When I need something built, Maya reviews my ask, consults the relevant docs, then pairs me with the right specialist. The product agent and CTO collaborate on PRDs and developer docs, which Claude Code executes.
It's like having a distributed team that never sleeps.
3. I Shipped Imperfection Strategically
My app has no auth system. Debugging artifacts litter the code. Styling is inconsistent. One CSS file handles some elements while others use inline styles.
This isn't laziness – it's strategy.
If beta users respond with "meh," then perfecting the architecture, implementing auth, and scaling infrastructure becomes wasted effort. Instead, I focused exclusively on core functionality: multi-model prompt testing that actually works.
This approach protected my time investment while accelerating user feedback. Sometimes good enough is perfect.
The Real Lesson
Speed beats perfection in early-stage validation. Especially right now with the speed that AI tools offer. By combining powerful tools (Claude Code), innovative workflows (AI teams), and strategic shortcuts (imperfect execution), I compressed months of development into weeks.
The beta results will determine if this becomes a business or a learning experience. Either way, I'll know in days, not months.