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Building a Production-Ready Go File Downloader in 3 Days with Claude AI

Building a Production-Ready Go File Downloader in 3 Days with Claude AI

How AI-assisted development helped create godl - a concurrent, resumable file downloader that achieved A+ Go Report Card grade

🚀 Introduction

Three days ago, I discovered node-downloader-helper, an excellent Node.js library for handling file downloads with features like resumable downloads, progress tracking, and concurrent connections. As a Go enthusiast, I wondered: "Could I build something similar in Go, but leverage AI to accelerate the development process?"

The result? godl - a production-ready, concurrent file downloader library and CLI tool that achieved:

  • 🏆 A+ Go Report Card grade (97% score)
  • 📊 Comprehensive test coverage with Codecov integration
  • 🔧 Dual interface - both library API and CLI tool
  • ⚡ Cross-platform support (Linux, macOS, Windows)
  • 🔌 Plugin architecture for extensibility

This article chronicles the 3-day journey of AI-assisted development and the lessons learned.

🎯 The Challenge

Building a file downloader sounds simple, but production-ready tools require:

  • Concurrent download management
  • Resume capability for interrupted downloads
  • Comprehensive error handling and recovery
  • Cross-platform compatibility
  • Clean API design
  • Proper testing and CI/CD

Normally, this would take weeks. Could AI help compress this timeline?

🛠️ The AI-Powered Development Stack

I decided to experiment with multiple AI tools to find the best approach:

  • Claude Desktop - Primary development assistant
  • Gemini - Architecture reviews and alternative perspectives
  • ChatGPT - Code optimization and documentation
  • Serena MCP - Advanced code analysis and refactoring
  • Notion + MCP - Project planning and documentation sync
  • .claude/CLAUDE.md - Context preservation across sessions

📅 Day 1: Foundation and Architecture

Morning: Project Initialization

Started with Claude Desktop to establish the project structure. The AI suggested a clean architecture:

godl/
├── cmd/godl/           # CLI application
├── pkg/                # Public API
├── internal/           # Private implementation
├── examples/           # Usage examples
└── docs/              # Documentation
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Key insight: AI immediately recognized the need for separation between public API (pkg/) and internal implementation (internal/), following Go best practices.

Afternoon: Core Architecture Design

Using input from Claude, Gemini, and ChatGPT, we designed the core interfaces:

// Main downloader interface
type Downloader interface {
    Download(ctx context.Context, url, destination string) (*Stats, error)
    DownloadWithOptions(ctx context.Context, url, destination string, opts *Options) (*Stats, error)
}

// Plugin system for extensibility
type Plugin interface {
    Name() string
    Execute(ctx context.Context, req *Request) (*Response, error)
}
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AI Contribution: Each AI provided different perspectives:

  • Claude: Focused on clean interfaces and Go idioms
  • Gemini: Suggested plugin architecture for extensibility
  • ChatGPT: Optimized for performance considerations

Evening: Initial Implementation

By end of day 1, we had:

  • ✅ Project structure
  • ✅ Core interfaces
  • ✅ Basic HTTP download functionality
  • ✅ Initial tests

📅 Day 2: Feature Implementation and CI/CD

Morning: Concurrent Downloads

The most complex feature - implementing concurrent chunk downloads:

type ConcurrentDownloader struct {
    maxConnections int
    chunkSize     int64
    client        *http.Client
}

func (d *ConcurrentDownloader) downloadConcurrently(
    ctx context.Context, 
    url string, 
    size int64,
    writer io.WriterAt,
) error {
    // AI-suggested approach: worker pool with bounded channels
    chunkChan := make(chan downloadChunk, d.maxConnections)

    // Launch workers
    for i := 0; i < d.maxConnections; i++ {
        go d.downloadWorker(ctx, url, chunkChan, writer)
    }

    // Generate chunks
    // ... implementation
}
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AI Magic: Claude suggested using io.WriterAt for concurrent writes to the same file - a Go idiom I wasn't familiar with.

Afternoon: Resume Functionality

For resumable downloads, AI helped implement partial content requests:

func (d *Downloader) resumeDownload(ctx context.Context, url, filepath string) error {
    // Check existing file size
    stat, err := os.Stat(filepath)
    if err != nil {
        return err
    }

    // AI-suggested: Use HTTP Range header for partial content
    req.Header.Set("Range", fmt.Sprintf("bytes=%d-", stat.Size()))
    // ... continue from where we left off
}
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Evening: CI/CD Pipeline

Used AI to create a comprehensive GitHub Actions workflow:

  • Multi-platform testing (Ubuntu, macOS, Windows)
  • Go Report Card integration
  • Code coverage with Codecov
  • Automated releases

Notion + MCP Integration: As features were completed, I used Notion's MCP integration to automatically update project documentation and sync progress with .claude/CLAUDE.md.

📅 Day 3: Code Quality and Polish

The Challenge: Cyclomatic Complexity

Go Report Card initially gave us a 78% score due to high cyclomatic complexity in several functions. Some functions had complexity scores of 20-30, well above the recommended limit of 15.

Serena MCP to the Rescue

This is where Serena MCP proved invaluable. While I can't directly compare it to other approaches (since this was my first experience), Serena's semantic code analysis capabilities were impressive:

// Before refactoring (complexity: 26)
func (d *Downloader) Download(ctx context.Context, url, dest string, opts *Options) (*Stats, error) {
    // 100+ lines of nested conditionals and error handling
}

// After refactoring (complexity: 12)
func (d *Downloader) Download(ctx context.Context, url, dest string, opts *Options) (*Stats, error) {
    if err := d.validateInputs(url, dest); err != nil {
        return nil, err
    }

    if err := d.prepareDownload(ctx, opts); err != nil {
        return nil, err
    }

    return d.executeDownload(ctx, url, dest, opts)
}
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Key Refactoring Strategies AI Suggested:

  1. Extract helper functions - Break complex functions into smaller, focused ones
  2. Use map lookups - Replace large switch statements with map-based lookups
  3. Early returns - Reduce nesting with guard clauses
  4. Separate concerns - Split validation, preparation, and execution logic

Results

After systematic refactoring guided by Serena:

  • 🎯 All functions ≤ 15 complexity
  • 📈 Go Report Card: A+ (97%)
  • 🧪 Comprehensive test coverage
  • 🚀 Production-ready codebase

📊 Final Results

After 3 days of AI-assisted development, godl achieved:

Metric Result
Go Report Card A+ (97%)
Test Coverage 90%+ (Codecov)
Cyclomatic Complexity All functions ≤ 15
Cross-platform ✅ Linux, macOS, Windows
Package Registry ✅ pkg.go.dev

Key Features Implemented

  • 🚀 Concurrent downloads with configurable connections
  • 📊 Real-time progress tracking with multiple formats
  • 🔄 Resume support for interrupted downloads
  • 🛡️ Comprehensive error handling with smart retry logic
  • 🔌 Plugin system for extensibility
  • 🌐 Dual interface - library API and CLI tool

🎓 Lessons Learned

What Worked Well

  1. Multi-AI Approach: Different AI models provided complementary perspectives

    • Claude: Go idioms and clean code
    • Gemini: Architecture and extensibility
    • ChatGPT: Performance optimization
    • Serena MCP: Deep code analysis

  2. Context Preservation: Using .claude/CLAUDE.md to maintain context across sessions was crucial for consistency

  3. Phase-by-Phase Documentation: Notion + MCP integration kept documentation synchronized with development progress

  4. AI-Guided Refactoring: Serena's semantic analysis made complex refactoring manageable

Challenges and Limitations

  1. AI Context Limits: Had to frequently re-establish context for complex architectural decisions

  2. Cross-Platform Testing: AI suggested approaches, but actual testing required multiple environments

  3. Domain Knowledge: AI accelerated implementation but required human judgment for architectural decisions

Best Practices Discovered

  1. Start with Clear Interfaces: AI works best when given well-defined boundaries
  2. Iterative Refinement: Build MVP first, then refactor with AI assistance
  3. Multiple Perspectives: Different AI tools catch different issues
  4. Context Documentation: Maintain detailed project context for AI assistants

🔮 Future Implications

This experiment demonstrated that AI-assisted development can significantly accelerate the creation of production-ready tools, but it's not about replacing human developers - it's about amplifying human capabilities.

The combination of:

  • Human architectural vision
  • AI implementation assistance
  • Automated quality tools
  • Continuous refinement

...can compress development cycles from weeks to days without compromising quality.

🚀 Try It Yourself

Want to experiment with godl?

As a Library 

go get github.com/forest6511/godl@v0.9.1
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package main

import (
    "context"
    "github.com/forest6511/godl"
)

func main() {
    stats, err := godl.Download(context.Background(), 
        "https://example.com/file.zip", "./downloads/file.zip")
    if err != nil {
        panic(err)
    }

    fmt.Printf("Downloaded %d bytes in %v\n", 
        stats.BytesDownloaded, stats.Duration)
}
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As a CLI Tool 

go install github.com/forest6511/godl/cmd/godl@latest

# Concurrent download with progress
godl --concurrent 8 --chunk-size 2MB https://example.com/file.zip
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🔗 Links

What's your experience with AI-assisted development? Have you tried building production tools with AI assistance? Share your thoughts in the comments!

go #golang #ai #claude #development #opensource #tools #productivity

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