Programming in Go - Essential Resources

Go continues to prove itself as a powerhouse for modern software development.

Whether you’re building microservices, CLI tools, or AI-powered applications, Go’s simplicity and performance make it an excellent choice.

Over time, I’ve compiled practical guides covering the essential aspects of Go development—from database interactions to AI integration. Here’s what you need to know.

1 Getting Started with Go ORMs

Database interactions are fundamental to most applications, and choosing the right ORM can significantly impact your productivity.

For PostgreSQL projects, I’ve explored the major players: Comparing Go ORMs for PostgreSQL: GORM vs Ent vs Bun vs sqlc provides a comprehensive comparison with practical code examples. If you need a quick decision framework, check out ORM to use in GO: GORM, sqlc, Ent or Bun? for guidance on matching the right tool to your project’s needs.

For those already using GORM, here’s a specific troubleshooting tip: Fixing Golang GORM AutoMigrate postgresql error addresses common migration issues you might encounter.

2 Building Production-Ready APIs

REST APIs form the backbone of modern services, and Go excels at building them efficiently.

Building REST APIs in Go: Complete Guide walks through everything from standard library approaches to popular frameworks, covering authentication, testing patterns, and production best practices.

Documentation matters just as much as implementation. Learn how to Adding Swagger to Go API with step-by-step instructions for generating OpenAPI documentation using swaggo and integrating Swagger UI with Gin, Echo, or Fiber.

For developers building command-line tools, Building CLI Applications in Go with Cobra & Viper demonstrates professional CLI development with robust configuration management.

3 AI Integration with Go

The AI landscape has exploded, and Go developers need practical ways to integrate LLM capabilities into their applications.

For working with Ollama, I’ve created detailed guides for both approaches: Using Ollama Web Search API in Go shows how to implement web_search and web_fetch functions, while Go SDKs for Ollama - overview with examples compares available Go clients with Qwen3 and GPT-OSS examples.

Looking at HTML parsing for AI workflows, Beautiful Soup Alternatives in Go covers Go libraries for web scraping and content extraction. For document generation, Generating PDF in GO - Libraries and examples provides practical examples for creating PDF reports.

Want to build sophisticated AI orchestration systems? Go Microservices for AI/ML Orchestration explores proven patterns for event-driven architectures and workflow engines.

The Model Context Protocol is becoming essential for AI tool integration. Model Context Protocol (MCP), and notes on implementing MCP server in Go explains message structure, protocol specifications, and provides an example implementation.

4 Advanced Architecture and Patterns

As your Go projects mature, architectural patterns become crucial for maintainability.

Multi-Tenancy Database Patterns with examples in Go explores shared database, separate schema, and database-per-tenant approaches with security trade-offs.

Understanding distributed transactions is essential for microservices: Saga Pattern in Distributed Transactions - With Examples in Go covers orchestration vs choreography, compensation strategies, and idempotency patterns.

Dependency Injection in Go: Patterns & Best Practices demonstrates constructor injection, interface design, and frameworks like Wire and Dig for testable, maintainable code.

5 Project Organization and Modern Go Features

Code organization can make or break large projects. Go Project Structure: Practices & Patterns guides you from flat structures to hexagonal architecture, explaining when to use cmd/, internal/, and pkg/ directories.

For multi-module development, Go Workspace Structure: From GOPATH to go.work covers modern workspace management with go.work files.

Go 1.18 introduced generics, fundamentally changing how we write reusable code. Go Generics: Use Cases and Patterns explores practical applications with best practices for type-safe, reusable code.

6 Testing and Quality

Robust testing practices separate hobby projects from production systems.

Go Unit Testing: Structure & Best Practices covers the built-in testing package, table-driven tests, mocks, and coverage analysis.

For performance optimization, Parallel Table-Driven Tests in Go explains how to leverage t.Parallel() effectively while avoiding race conditions.

Quality assurance extends beyond testing. Go Linters: Essential Tools for Code Quality provides a complete guide to golangci-lint, staticcheck, and CI/CD integration strategies.

7 Wrapping Up

Go’s ecosystem continues to mature, offering robust solutions for everything from traditional web services to cutting-edge AI applications. The resources above represent practical, battle-tested approaches to common challenges.

Whether you’re just starting with Go or optimizing production systems, these guides should help you write cleaner, more efficient code. Happy coding!