From Concept to Production in 8 Hours: How Amazon Q Developer CLI Revolutionized My Development Workflow

Building a complete CLI tool with AI assistance - a detailed journey through modern software development

Introduction

Last week, I accomplished something that would have seemed impossible just a few years ago: I took an idea for a CLI tool and delivered a complete, production-ready application with full CI/CD pipeline, cross-platform distribution, and comprehensive documentation in just 8 hours. The secret? Strategic use of Amazon Q Developer CLI with Claude 4 Sonnet, combined with deep knowledge of the Go ecosystem and DevOps practices.

The result is httpcode - a beautiful command-line tool for looking up HTTP status codes with interactive fuzzy search, styled terminal UI, and cross-platform support.

The Challenge: Traditional Development Timeline

Typically, building a production-ready CLI tool involves weeks of work:

• Week 1: Architecture design and core implementation

• Week 2: Testing, error handling, and edge cases

• Week 3: CI/CD pipeline setup and deployment automation

• Week 4: Documentation, packaging, and distribution

This timeline assumes you're already familiar with the tools and frameworks. For someone learning, it could easily stretch to months.

The AI-Assisted Approach: Strategic Tool Selection

As an experienced Go programmer and CLI enthusiast, I knew exactly what tools would deliver the best user experience:

Core Libraries

• Cobra: The de facto standard for Go CLI applications, providing not just a robust framework but automatic shell completion generation for bash, zsh, fish, and PowerShell

• Lipgloss: For beautiful, color-coded terminal styling that makes the tool a joy to use

• fzf: For smooth, interactive fuzzy search functionality

The Go Advantage

Choosing Go wasn't just about personal preference - it provided strategic advantages:

• True cross-platform support: Single codebase compiles to native binaries for Mac, Linux, Windows

• Zero runtime dependencies: Compiled binaries work out-of-the-box

• Excellent tooling ecosystem: Rich libraries and mature CI/CD integrations

Hour-by-Hour Breakdown

Hours 1-2: Architecture and Core Implementation

Using Amazon Q Developer CLI, I prompted for the basic project structure:

Create a Go CLI application using Cobra that:

• Looks up HTTP status codes by number, lists codes by category (1xx, 2xx, etc.)

• Provides interactive fuzzy search

• Use fzf as library instead of reimplement it with tcell2

• Add detail and MDN links

The AI generated a well-structured application with:

• Proper command hierarchy

• Clean separation of concerns

• Idiomatic Go code patterns

• Error handling best practices

See https://github.com/lethang7794/httpcode/compare/hour-0...hour-2

Hours 3-4: Enhanced Features and Styling

Next, I focused on the user experience:

Enhance the CLI with:

• Color-coded categories (blue for 1xx, green for 2xx, etc.) by using Lipgloss for beautiful terminal styling

• Improve UX

• Enhance fuzzy search with detailed preview

• Shell completion support

• Professional-looking help text

See https://github.com/lethang7794/httpcode/compare/hour-2...hour-4

Hours 5-6: Testing and Quality Assurance

I prompted for comprehensive testing, create tests for:

• Core HTTP status code lookup functionality

• List command with category filtering

• Display formatting functions

• Command argument parsing

• Error handling scenarios

The result was a solid test suite covering all main functionality, ensuring reliability and maintainability.

See https://github.com/lethang7794/httpcode/compare/hour-4...hour-6

Hours 7-8: CI/CD Pipeline and Distribution

This is where my DevOps knowledge became crucial. Instead of spending days configuring complex pipelines, I could prompt precisely:

Create a GitHub Actions workflow that:

• Runs tests on Go 1.23.0

• Uses semantic versioning based on commit messages

• Integrates with GoReleaser for cross-platform builds

• Creates GitHub releases with binaries for multiple architectures

• Sets up Homebrew tap for easy installation

The AI generated a sophisticated CI/CD pipeline with:

• Automated testing and quality gates

• Semantic versioning from conventional commits

• Cross-platform binary builds (amd64, arm64)

• Automated GitHub releases

• Homebrew tap integration

See https://github.com/lethang7794/httpcode/compare/hour-6...hour-8

The Power of Strategic Prompting

The key to this rapid development wasn't just using AI - it was knowing exactly what to ask for. My experience with:

• Go ecosystem: Enabled me to choose the right libraries and architectural patterns

• CLI best practices: Informed decisions about user experience and interface design

• DevOps workflows: Allowed precise prompting for complex CI/CD requirements

• GoReleaser: Simplified what could have been weeks of release automation work

Technical Deep Dive: Key Features

Interactive Fuzzy Search

The tool includes built-in fuzzy search functionality that allows users to:

• Type to filter HTTP status codes in real-time

  

• Navigate with arrow keys

  

• View detailed information in a preview pane

• Select codes with Enter or exit with Ctrl+C

  

Beautiful Terminal Styling

Using Lipgloss, each HTTP status code category has its own visual identity:

• 🔵 1xx (Informational): Blue styling

• 🟢 2xx (Success): Green styling

• 🟠 3xx (Redirection): Orange styling

• 🔴 4xx (Client Error): Red styling

• 🟣 5xx (Server Error): Purple styling

Cross-Platform Distribution

The automated pipeline builds binaries for:

• Linux: amd64, arm64

• macOS: amd64, arm64 (including Apple Silicon)

• Windows: amd64, arm64

Shell Integration

Cobra automatically generates completion scripts for:

• Bash

• Zsh

• Fish

• PowerShell

The Results: Production-Ready in Record Time

After 8 hours, I had delivered:

• ✅ Beautiful CLI tool with intuitive interface

• ✅ Core functionality tested with comprehensive test suite

• ✅ Automated CI/CD pipeline with semantic versioning

• ✅ Cross-platform distribution for all major operating systems

• ✅ Package manager integration via Homebrew

• ✅ Complete documentation with usage examples and installation guides

Installation and Usage

The tool is available through multiple channels:

• Mac/Linux (Homebrew):

    brew tap lethang7794/tap
    brew install httpcode
  

• Windows/Others:

  Download from GitHub Releases

• If you're a Gopher:

    go install github.com/lethang7794/httpcode@latest
  

Usage

# Interactive fuzzy search
httpcode

# Look up specific code
httpcode 404

# List all code of all category
httpcode list

# List by category
httpcode list 4xx

# Get help
httpcode --help

# See current version
httpcode version

Lessons Learned: AI as a Force Multiplier

This project demonstrated several key insights about AI-assisted development:

1. Domain Knowledge is Critical

AI doesn't replace expertise - it amplifies it. My knowledge of Go, CLI design, and DevOps practices enabled precise prompting that generated production-quality results.

2. Strategic Tool Selection Matters

Choosing mature, well-designed libraries (Cobra, Lipgloss, GoReleaser) meant the AI could leverage existing best practices rather than reinventing solutions.

3. Iterative Refinement Works

Rather than trying to generate everything at once, breaking the project into focused prompts allowed for better results and easier debugging.

4. Testing and Quality Can't Be Skipped

Even with AI assistance, proper testing and quality assurance remain essential for production software.

The Future of Development

This experience showcases the potential of AI-assisted development when combined with human expertise. AI is not replacing developers - AI is creating a new paradigm where experienced professionals can deliver production-quality software at unprecedented speed.

The key is understanding that AI works best as a sophisticated tool in the hands of someone who knows what they want to build and how to evaluate the results.

Conclusion

Building httpcode in 8 hours wasn't just about speed - it was about demonstrating a new approach to software development. By combining AI assistance with strategic technical knowledge, we can compress traditional development timelines while maintaining quality and best practices.

The future belongs to developers who can effectively collaborate with AI tools, using their expertise to guide and refine AI-generated solutions into production-ready software.

Try httpcode today and experience the result of this AI-assisted development approach. The source code is available on GitHub, showcasing how modern development practices can deliver beautiful, functional software in record time.