When I started planning my second AI project—a video generation platform—I realized I had a choice: build on top of my first project’s codebase or start completely over. I chose to rebuild everything. Here’s why, and what I learned about software architecture along the way.
When Success Exposes Weaknesses
My first AI Music Generator worked and users loved it. But success also revealed architectural flaws:
- Bug fixes broke unrelated features
- Adding new AI providers took weeks instead of hours
- User credit handling was fragile
- Database queries were scattered everywhere
- Constants were defined randomly across the project
This was technical debt in action. Like financial debt, it compounds over time.
Rebuild or Refactor?
I spent weeks weighing whether to refactor or rebuild. The tipping point came when I tried adding a new music provider—what should have been a two-day task became a three-week nightmare untangling spaghetti code. I realized the system wasn’t built on solid foundations.
Rebuilding from Scratch: Architecture Lessons
Database Design Matters
Music Generator: I added tables as needed, sometimes without foreign keys or proper relationships.
Video Platform (WAN S2V): I spent two weeks designing the database schema. Every table relationship is intentional, constraints documented, and migrations carefully planned.
Result: Adding video generation progress tracking fit naturally into the schema without changes or migration headaches.
Service Layer > Spaghetti Code
Music Generator: Business logic lived everywhere—API routes, React components, utilities—finding anything was a treasure hunt.
Video Platform (WAN S2V): I created a proper service layer:
-
GenerationService: handles AI generation logic -
CreditService: manages user credits and transactions -
MediaService: processes and stores generated content -
UserService: authentication and user management
Result: Bugs are easier to trace, and modifying core logic is centralized.
External APIs Need Abstraction
Music Generator: Each AI provider had a unique implementation—different error handling, retries, and response formats. Adding Suno or MusicLM took weeks.
Video Platform (WAN S2V): I built a unified AI SDK:
const result = await aiSDK.callAndWaitForCompletion(
'provider-name',
'model-name',
parameters,
progressCallback
);
Result: Adding VEO3 took 4 hours; WAN22 models 2 hours each. The pattern scales easily.
Configuration Over Hard-Coding
Music Generator: Adding a new music style meant modifying components, constants, API routes, and testing everything.
Video Platform (WAN S2V): Adding new tools is configuration-driven. Adding speech-to-video was as simple as:
'speech-to-video': {
label: 'Speech to Video',
component: VideoGeneration,
models: { 'wan22s2v': 'WAN22 S2V' },
status: 'available'
}
UI, API routing, and generation flow worked automatically.
Mindset Shift
- From “Make it Work” to “Make it Maintainable”: I now consider long-term stability, not just immediate functionality.
- From scattered constants to organized systems: Constants are now grouped by domain, making future updates predictable.
- From reactive to proactive error handling: I design for error scenarios upfront—AI provider downtime, insufficient credits, failed uploads, or users closing their browsers.
Credit System: Complexity Made Clear
Rebuilding the credit system was the hardest part. The previous approach charged credits at generation start, leading to double charges, partial failures, and inconsistent refunds.
New approach: Freeze → Generate → Consume or Refund. This eliminates double charges and ensures a predictable user experience, trading implementation complexity for reliability.
Developer Growth
- Patience over speed: Taking time to plan architecture upfront saved months of refactoring later.
- Systems thinking: Software architecture is like a building—database is the foundation, service layer are load-bearing walls, API layer is the exterior, frontend is the interior design. A weak foundation destabilizes everything.
- Embracing predictable solutions: Clever code is impressive; predictable, boring code is maintainable and scalable.
Results: Metrics Matter
| Metric | Music Generator | Video Platform (WAN S2V) |
|---|---|---|
| Development time | 3–5 days per new provider | 2–4 hours per AI model |
| Bug reports | 8–10/week | 2–3/month |
| Confidence in features | High stress each deployment | Confident deployment with rollback strategy |
| Maintenance focus | 40% fixing old code | 80% building new features |
Lessons for Version 3
- Start with tests to catch edge cases early
- Plan for internationalization from day one
- Consider mobile and responsive design upfront
- Document architectural decisions and rationale
Advice for Developers on Their Second Project
- Don’t be afraid to rebuild—leverage lessons from your first project to build something better.
- Invest in architecture early; short-term slowness compounds into long-term speed.
- Think in systems, not just features. Consider how new functionality interacts with the whole.
- Plan for growth. How does your system behave with 10x or 100x more users?
The Platform Today
WAN S2V now supports multiple AI models (VEO3, WAN22 series) and can generate videos from text, images, and speech. It’s built to scale and evolve.
The difference between your first and second project: the first teaches you what’s possible, the second teaches you what’s sustainable.
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