Vibe Animation-Coding: 6 things I learned from this new uncharted content creation pattern

A self-reflective notes for future code-to-video projects and the unexpected lessons that emerged

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Read about the FlappyAngryBird project’s Part 1 and Part 2 here

This article also posted on my Medium page

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expanding the knowledge horizon

“Vibe animation-coding” isn’t a term you’ll find in any documentation (I just made it up but I have a strong believe that this will start being more common soon). But it captures something unique about this approach: the ability to iterate on creative ideas at web development velocity, where you’re not just writing code to solve technical problems, but using code as a creative medium with the same fluidity you’d expect from a design tool.

The thing is, this territory is completely uncharted. There are no best practices, no established patterns, no Stack Overflow / Reddit answers for “how to architecturally organize your bird-gets-struck-by-lightning animation system.” You’re making it up as you go, which means you’re going to make mistakes. Lots of them.

After building a sophisticated 15-second animation with dynamic waypoint systems, advanced visual effects, and production optimization, then writing 6,300+ words documenting the entire process, I’ve collected some hard-won insights that I wish I’d known when I started. These aren’t just animation tips — they’re lessons about creative coding, AI collaboration, and the intersection of engineering discipline with artistic expression.

Here are the 7 things I learned from this new world of vibe animation-coding, including the mistakes that taught me the most.

1. The Refactoring Trap: When “Better Structure” Makes Everything Worse

The Lesson: Sometimes the solution isn’t better organization of the wrong approach — it’s a completely different approach.

My FlappyAngryBird animation started with hardcoded waypoint arrays — dozens of `{ frame: 52, targetY: 443 }` objects scattered throughout the code. As the animation grew more complex, this became unmaintainable. Changing one timing meant manually recalculating frame numbers for everything that followed.

So I decided to “refactor” by creating a more organized structure:

// My brilliant first refactor attempt 🤦‍♂️
const waypointSections = {
 intro: { frames: [0, 30], waypoints: […] },
 pipes1to3: { frames: [30, 120], waypoints: […] },
 difficulty_spike: { frames: [120, 200], waypoints: […] },
 // … even more nested complexity
};

This was objectively worse than the original hardcoded arrays. More code, more complexity, but zero improvement in the actual problem I was trying to solve. I had fallen into what I now call the “refactoring trap” — adding more structure to the wrong solution.

The breakthrough came when I stopped asking “how can I better organize this hardcoded data?” and started asking “how can I edit flight paths in real-time?” That reframing led to a complete architectural shift: a database-driven waypoint system with CRUD operations, localStorage persistence, and real-time synchronization.

In broader context: When you’re solving the wrong problem, adding more structure is like organizing your junk drawer — it’s still junk, just in neat piles. Step back and question whether you’re solving the right problem in the first place.

Future application: Any time you find yourself creating more complex configuration objects or nested data structures, pause and ask: “Am I organizing the problem or solving it?” Sometimes the answer is to throw out the entire approach and start fresh.

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2. AI Collaboration is Like Dating: Specificity Gets Results

The Lesson: Vague prompts get vague results. Specific, contextual conversations unlock AI’s real potential.

My early AI collaboration attempts were embarrassingly generic:

”Make my animation better” → Generic suggestions about timing and easing functions

”Fix the performance” → Boilerplate optimization advice that didn’t address my actual bottlenecks

”Help with the waypoint system” → Basic array manipulation examples

Then I learned to be specific about context and constraints:

”My React component re-renders on every frame because particle calculations happen in the render function. I need to memoize these calculations while keeping the animations smooth. The particles need individual timing offsets and should respond to props changes.”

This specificity led to targeted, implementable solutions: React.use Memo patterns, dependency array optimization, and component architecture suggestions I hadn’t considered.

The real breakthrough was treating AI assistance as collaborative design rather than automated problem-solving. The best conversations were iterative:
1. Describe the problem and constraints
2. Review the suggested approach
3. Ask follow-up questions about trade-offs
4. Request variations or refinements
5. Discuss edge cases and error handling

In broader context: AI assistants are like humans — they need context to be helpful, not just “fix my life.” The quality of output is directly proportional to the quality of input.

Future application: Before asking for help, prepare the context: What are you trying to accomplish? What constraints do you have? What have you already tried? What specific outcome do you want? This applies to human collaboration too, by the way.

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3. Performance Optimization: Profile First, Optimize Second (Or Third)

The Lesson: Measure actual bottlenecks rather than guessing; simple solutions often have bigger impact than complex ones.

I was convinced my animation performance issues were due to complex mathematical calculations. I spent hours creating sophisticated caching systems for interpolation functions:

// My overengineered “optimization” 🙄
const frameCache = new WeakMap();
const optimizedInterpolate = (frame, inputRange, outputRange) => {
 const cacheKey = `${frame}-${inputRange.join(‘-’)}-${outputRange.join(‘-’)}`;
 if (frameCache.has(cacheKey)) return frameCache.get(cacheKey);
 
 const result = interpolate(frame, inputRange, outputRange);
 frameCache.set(cacheKey, result);
 return result;
};

This “optimization” added complexity, introduced potential memory leaks, and optimized functions that were already running in microseconds. Meanwhile, my animation was dropping frames because particle components were re-rendering unnecessarily, causing 45ms render times instead of the target 16ms.

The actual fix was embarrassingly simple:

// The real solution 🎯
const particleProps = useMemo(() => calculateParticleProperties(), []);

One line of React.useMemo reduced frame render times from 45ms to 12ms — far more impact than my elaborate caching schemes.

In broader context: I was polishing the doorknob while the house was on fire. Browser performance tools exist for a reason. Use them before optimizing anything.

Future application: For any performance problem, open the browser’s performance profiler first. Measure actual bottlenecks, not assumed ones. Often the simplest optimizations have the biggest impact.