When running cross-border or globally distributed systems, the physical latency and bandwidth cost of transoceanic networks have always been a problem we cannot avoid.
Author background: Backend development, self-taught LLM fine-tuning in spare time. This article records the complete process of a personal hands-on project (RoboThink) from model selection, training, quantization, to local deployment, focusing on the pitfalls I encountered, issues that only became clear in hindsight, and unverified assumptions. The project results are not flashy, but the lessons learned along the way are very valuable to me, so I organized them into this write-up.
A user says to the microphone, "Remind me to have a meeting tomorrow morning at eight." Then Gemini says "Got it," and the conversation continues. At exactly eight o'clock the next morning, a voice actually sounds out from the speaker saying, "It's time for the meeting." What actually happens in between? While building my own private AI assistant, I implemented this reminder mechanism. The entire link runs all the way from the microphone to the speaker, passing through speech recognition, tool calling, database, Redis events, WebSocket, and finally back to Gemini Live's speech synthesis—yet without a single line of polling code, and without introducing any task queue frameworks.
Previously, I wrote an introductory article about Firebase Genkit for Go, discussing the basics of the AI framework launched by Google specifically for Go developers. However, getting knowledge from books is shallow; writing Hello World officially is completely different from building a real private butler system that runs in the background and is used heavily every day. This note is a complete practical review of building my exclusive digital butler Iris from scratch, based on the Go language, the Google Gemini Live native multimodal protocol, Genkit, PostgreSQL (pgvector), and Redis.
Target Audience: Backend engineers with a solid foundation in Go who want to get started with large language model (LLM) application development but don't know where to begin. Reading Time: About 20 minutes. Official Repository: I recently integrated an LLM into a Go backend project, and after hitting quite a few roadblocks, I finally figured out a smooth approach. This article summarizes the entire process and mindset to share with friends struggling with the same things.
Author: A thoroughly awakened backend engineer. I first met it on a sultry afternoon in a college dorm. That day, a roommate casually tossed me a copy of *Python Crash Course* and said, 'Learn this, and you can write a web scraper in three days.' I opened the first page, saw that line of print("Hello, World!") as clean as poetry, and somewhere inside my heart, I quietly caved. From that moment on, I knew I was in love. For the first two years we were together, our life was extravagantly lavish...
> Current Project Version: `v1.0.6` > Project URL: <https://github.com/unclesam-ly/git-air>
> Author: UncleSam > Project open source address: [github.com/unclesam-ly/g
If you are using Go to build LLM-powered applications or AI Agents with Gemini, handling untrusted user input safely is a critical architecture requirement.
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