Iteration 1: Scaffolding the Audio Service

experiences , ai-assisted-development
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This is part of the First Challenge: Building a WebAPI with .NET Aspire Stack series.

My first deep dive with Claude Code was quite the adventure! I decided to build an audio processing service with .NET - something with an API project and a background worker. Here’s how it went down and what I learned along the way.

The Initial Goal: Keep It Simple

The plan was straightforward: create a basic .NET backend with:

  • An API project for normal user CRUD operations
  • A background worker service (AudioProcessor)
  • Standard audio processing workflows

Claude Code was enthusiastic, happily outputting project structures and sample codes. Of course, the initial code didn’t work (as expected), but that’s part of the learning process.

The Claude Code Experience

The Good

  • Fast scaffolding: Claude generated project structures quickly
  • Day 1 success: We ended up with a compilable solution that looked very promising
  • Creative problem-solving: Claude was great at suggesting approaches and alternatives

The… Interesting

  • Auto-compacting chaos: Claude Code hit auto-compact many times, which made the code get whacky sometimes
  • Code destruction and recreation: Claude would destroy and rewrite code constantly. Since it was all Claude’s example code anyway, this was fine - our aim was just to get a working version
  • Overly confident responses: I learned to treat words like “Absolutely” and “Perfectly” as just decorative messages

When Imagination Runs Wild: The Architecture Phase

After the basic solution compiled, I began sketching a scalable architecture. I imagined a system where nodes automatically scale using consistent hashing, with each node either running the background AudioProcessor or serving the WebAPI. Configuration for a reverse proxy would live in code, providing a single source of truth.

A quick Google search pointed me to Envoy. When I asked Claude Code if Envoy could fit into our design, the reply was “Absolutely!”

We designed a comprehensive system that would:

  • Scale automatically with consistent hashing load balancing across different nodes
  • Run either background workers to process files OR serve WebAPI requests
  • Be configured by a reverse-proxy with configuration in code for single source of truth
  • Use Envoy for load balancing
  • Include a SignalR hub for real-time processing progress via WebSocket
  • Leverage Redis Pub/Sub for local performance, with potential Cloud Pub/Sub service integration
  • Support local file system for development without needing Cloud Storage
  • Scale to Cloud Storage when needed

The result was a comprehensive design document. However, Claude Code repeatedly hit context-length limits and auto-compacted the session, so our vision remained on paper rather than in code.

I learnt to treat “Absolutely” and “Perfectly” as just… decorations. 😅

The Reality Check

We came up with a beautiful structure and design document. The architecture was sound, the patterns were modern, and the scalability story was compelling.

But here’s the kicker: all attempts at implementation failed.

As expected, really. The massive architecture we imagined was simply too much workload for Claude Code to handle in one go. We hit context limits repeatedly, went through numerous compacting cycles, and the complexity just spiraled beyond what could be effectively managed in a single session.

Key Learnings

The Real Experiment: Pushing Boundaries

Look, everyone knows to “keep scope small and manageable” - that’s Software Engineering 101. But what I was really doing here was deliberately pushing the imagination boundaries with Claude Code. This wasn’t about building a production system; it was about exploring:

  • How far can we stretch the architecture imagination?
  • What happens when we dream big with AI assistance?
  • Where are the actual limits of AI-assisted design?

The exercise was educational precisely because we went beyond the sensible boundaries. By intentionally designing a complex, distributed system in one go, I learnt exactly where Claude Code excels (creative system design, rapid ideation) and where it struggles (maintaining context for complex implementations).

About Claude Code

  1. Great for scaffolding and exploration - Perfect for getting started and trying ideas
  2. Context limits are real - Complex architectures need to be broken down into smaller chunks
  3. Manage expectations - Treat confident responses as enthusiastic suggestions, not guarantees
  4. Code churn is normal - Don’t get attached to any particular implementation

About AI-Assisted Development

  1. Imagination exercises are valuable - Pushing boundaries teaches you the tool’s limits
  2. Focus on compilable iterations - Getting something that builds is a good milestone
  3. Architecture first, implementation second - The design phase was actually very valuable
  4. Embrace the chaos - The code destruction/recreation cycle is part of the process

About Learning Through Extremes

  1. Massive architectures reveal tool limitations - You learn more by pushing boundaries
  2. “Failing” at implementation still succeeds at learning - The journey matters
  3. Document the vision - Even if implementation fails, good design documents have value

What’s Next?

The first iteration taught me a lot about working with Claude Code effectively. For the next round, I’m planning to:

  • Start with smaller, focused components
  • Build incrementally rather than attempting the full architecture
  • Use the design document as a roadmap rather than trying to implement everything at once
  • Treat Claude’s enthusiasm as motivation, not as project guarantees

Despite the implementation challenges, this was incredibly educational. The process of imagining and designing complex systems with AI assistance opened up new ways of thinking about architecture and rapid prototyping.

Sometimes the journey is more valuable than the destination - and this first iteration was definitely an enlightening journey! 🚀

The Takeaway

Working with Claude Code is like having an enthusiastic coding partner who never gets tired and always has ideas. Just remember to:

  • Keep scope manageable
  • Break complex problems down
  • Expect iteration and rework
  • Focus on learning over perfect implementation

Ready for iteration two! 💪

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