DevLog #3 - Building Products Alone: AI-Assisted Development

Have you ever watched a developer frantically switch between coding, designing, and product planning—all within the same hour? That’s been my reality while building Validatelite, and honestly, it’s both exhilarating and exhausting.

In the age of AI-assisted development, the traditional boundaries between roles are dissolving. One person can now handle what previously required an entire team. But here’s the catch: when you’re wearing every hat, each decision becomes exponentially more critical.

The Role Revolution in AI-Driven Development

When Claude Code and Cursor can generate thousands of lines of code in minutes, why do we still need product managers, designers, and QA engineers? The answer surprised me: we don’t eliminate these roles—we internalize them.

Think about it. In traditional teams, a product manager challenges questionable technical decisions, designers push back on poor user experiences, and testers catch hidden bugs. These checks and balances naturally occur through collaboration. But when you’re flying solo with AI-assisted development, all those voices need to exist in your head.

This isn’t about replacing five people with one person plus AI. It’s about one person developing the product mindset of five professionals.

Slow is Fast: The Counter-Intuitive Truth

Here’s my embarrassing timeline from the first attempt:

Day 1:  Excitedly let AI generate code  
Day 5:  Architecture problems, major refactoring needed
Day 10: Realized I misunderstood the requirements, restart
Day 16: Data model design flaws, complete rebuild

Sound familiar? I was moving fast and breaking everything—including my own motivation.

After reflection, here’s what actually worked:

Day 1:   Research and problem definition (zero code)
Day 2-3: Design and prototyping (still no code)  
Day 4:   Architecture planning (you guessed it—no code)
Day 5-6: AI-driven development begins
Day 7-15: Testing and optimization
Day 16-17: Successful launch

Those first four “unproductive” days of thinking? They eliminated weeks of rework. In AI-assisted development, the cost of changing direction increases exponentially after you start coding.

Validatelite: A Solo Developer’s Product Journey

Stage 1: Problem Definition (Business Analyst Hat)

I resisted the urge to open my IDE and instead played business analyst. The key questions weren’t technical:

• What specific pain point am I solving?
• Who experiences this pain most acutely?
• How do existing solutions fall short?
• What would success look like for users?

For Validatelite, I discovered that data engineers spend countless hours manually checking data quality. The existing tools were either too complex for quick validation or too simple for real-world scenarios.

Stage 2: Design-First Approach (UX Designer Hat)

With the problem clear, I switched to UX designer mode. But instead of fancy mockups, I used ASCII diagrams:

┌─────────────────┐
│   CLI Layer     │
│                 │
├─────────────────┤
│ Core Engine     │
│ Layer           │
│                 │
├─────────────────┤
│ Shared          │
│ Infrastructure  │
└─────────────────┘

Dependency Flow: CLI → Core → Shared
Strict Rules: No reverse deps, no cross-layer deps

Why ASCII instead of beautiful SVG diagrams? Because it forces focus on function over form. No colors, fonts, or animations to distract from the core information architecture.

Since Validatelite is a CLI tool, I spent most design time thinking about command structure and parameter organization—the real user experience for developers.

Stage 3: Architecture Thinking (Technical Lead Hat)

Now wearing my architect hat, I established core principles:

• Core Independence: Validation engine decoupled from infrastructure
• CLI-First: Command-line interface for rapid validation startup
• Clean Dependencies: Strict control over module relationships
• Single Execution Entry: One unified interface for rule execution

These weren’t just technical decisions—they were product strategy encoded in architecture.

Stage 4: AI-Assisted Implementation (Developer + QA Hat)

Finally, development time! With clear design and architecture, AI tools became incredibly effective. I developed and tested simultaneously, ensuring each feature worked before moving forward.

Without the upfront preparation, this phase would have taken months with constant rework. Instead, it was smooth and predictable.

The New AI Native Team Model

Evolution, Not Replacement

AI native teams don’t replace five people with one person plus AI. They create professionals with complete product mindset. In traditional teams, developers could say “that’s a product problem” and designers could claim “technically impossible.”

In AI-driven development, everyone owns the final outcome.

New Forms of Collaboration

Even in the AI era, teamwork matters—the format just evolved:

• Code reviews became thought process reviews
• Task distribution became parallel exploration
• Meeting discussions became prototype conversations

Teams now communicate through working code rather than lengthy specifications.

Essential Advice for AI-Assisted Development

Develop Product Mindset

Don’t just focus on technical implementation. Understand why users need this feature, what real problem it solves, and whether simpler solutions exist. Technology is the means; problem-solving is the purpose.

Learn to Slow Down

AI can help you write 1,000 lines in an hour, but spend 3 hours thinking first. Do those 1,000 lines solve real problems? Is the architecture sound? Will it be maintainable? Without clear thinking, more code just means more waste.

Maintain Critical Thinking

AI-generated code often looks “perfect,” but stay vigilant. Is this truly the optimal solution? Are there hidden issues? Is it over-engineered? Don’t let surface-level perfection fool you—dig into the essence.

Critical Documentation Never Disappears

Even when AI can generate everything, these documents must remain human-controlled:

• PRD defines what the product is
• User flows describe how it’s used
• Data models establish information structure
• API design defines module boundaries

These aren’t just documents—they’re crystallized thinking.

Testing in AI-Driven Development

Validatelite invested heavily in testing because reviewing AI-generated code solo is nearly impossible. I maintained 80% test coverage, but since AI also wrote the tests, quality control became crucial.

AI’s main testing problem: it can’t distinguish between test failures and main code issues. It often downgrades requirements after multiple failures. My approach:

1. Write test designs first - require AI to generate test cases, methods, and frameworks
2. Correct AI drift - redirect when AI deviates from test plans
3. Trust first attempts - AI’s initial test code usually reflects its true requirement understanding
4. Take control - after multiple AI failures, either manually handle testing or try different models

Final Thoughts

AI is a powerful executor, but it needs a clear commander. When you’re simultaneously product manager, designer, and developer, you must switch between roles fluidly. But never forget: thinking time is never wasted—skipping thought is always the most expensive mistake.

The ancient wisdom “sharpening the axe doesn’t delay cutting wood” has new meaning in the AI era: AI is that incredibly sharp axe, and your thinking determines which tree to cut.

Remember: in an age where code can be generated in seconds, those willing to spend days thinking are the real winners.

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P.S. Check out my open source project Validatelite

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Recommended Reading:

• DevLog #1 - ValidateLite: Building a Zero-Config Data Validation Tool: Follow the initial development journey and MVP progress
• DevLog #2 - Rethinking My Data Validation Tool: From Scope Creep to a True MVP: Learn how cutting scope creep led to a successful launch

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