Go

As AI and ML workloads become increasingly complex, the need for robust orchestration systems has become greater. Go’s simplicity, performance, and concurrency makes it an ideal choice for building the orchestration layer of ML pipelines, even when the models themselves are written in Python.

Building high-performance REST APIs with Go has become a standard approach for powering systems at Google, Uber, Dropbox, and countless startups.

Go’s built-in testing package provides a powerful, minimalist framework for writing unit tests without external dependencies. Here are the testing fundamentals, project structure, and advanced patterns to build reliable Go applications.

The Saga pattern provides an elegant solution by breaking distributed transactions into a series of local transactions with compensating actions.

Generics in Go represent one of the most significant language features added since Go 1.0. Introduced in Go 1.18, generics enable you to write type-safe, reusable code that works with multiple types without sacrificing performance or code clarity.

Multi-tenancy is a fundamental architectural pattern for SaaS applications, allowing multiple customers (tenants) to share the same application infrastructure while maintaining data isolation.

Command-line interface (CLI) applications are essential tools for developers, system administrators, and DevOps professionals. Two Go libraries have become the de facto standard for CLI development in Go: Cobra for command structure and Viper for configuration management.

AWS Kinesis has become a cornerstone for building modern event-driven microservices architectures, enabling real-time data processing at scale with minimal operational overhead.

Developers often face the “works on my machine” dilemma due to dependency mismatches, tool versions, or OS differences. Dev Containers in Visual Studio Code (VS Code) solve this elegantly — by letting you develop inside a containerized environment configured specifically for your project.

This guide provides a comprehensive overview of available Go SDKs for Ollama and compares their feature sets.

Large Language Models (LLMs) are powerful, but in production we rarely want free-form paragraphs. Instead, we want predictable data: attributes, facts, or structured objects you can feed into an app. That’s LLM Structured Output.

Most prominent ORMs for GO are GORM, Ent, Bun and sqlc. Here is a little comparison of them with examples of CRUD operations in pure GO.

Here we have a description of The Model Context Protocol (MCP), short notes on how to implement an MCP server in Go, including message structure, protocol specifications.

Go’s ecosystem offers a range of ORM (Object-Relational Mapping) tools and database libraries, each with its own philosophy. Here is a comprehensive comparison of four major solutions for using PostgreSQL in Go: GORM, sqlc, Ent, and Bun.

Here’s basic Go program structure, error handling patterns and channels vs goroutines comparison.