
While designing a real-time data streaming platform, I was faced with a tricky decision:

his article does not require you to have a deep technical background. If you know "what the Go language is" and can understand a tiny bit of code, you will be able to finish reading this article and figure out how the underlying layer of a complex system actually runs. If you have never heard of Go, that's

Have you ever wondered how AI accurately finds the answer when you ask "What is this year's annual leave policy?" in your company's knowledge base?

As a programmer who deals with code daily, I have recently noticed some subtle changes in the web traffic ecosystem.

he capabilities of Large Language Models (LLMs) need no further introduction, but "being able to chat" and "being able to call" are two completely different things.

When designing a real-time data or logging pipeline, alerting is a fundamental requirement.

When starting out with servers, this is how most of us troubleshoot issues:

In the Go backend ecosystem, the combination of 'Gin' and 'GORM' has long occupied small and medium-sized projects due to its extremely low threshold to get started

Some time ago, I designed and open-sourced **Gophe

Target Keywords:** Golang multi-LLM client, OpenAI compatible API Go, DeepSeek Go API, Gemini Go API, Go OpenAI client pool, LLM provider fallback Golang, go-openai multi-provider

> - **Target Keywords:** Golang LLM JSON parsing, OpenAI JSON mode Go, json.Decoder vs json.Unmarshal, robust LLM integration Go, parse LLM response Golang.

AI Agent Memory System, Golang AI Agent, Gemini Embeddings, pgvector PostgreSQL, Semantic Search Go, GORM pgvector, Vector Database Go