A few days ago, I built a local virtual assistant in Python (JARVIS) using Ollama , PyQt6 , and Piper TTS . It was a fun MVP, but as I started using it daily, the architectural flaws became obvious. Hardcoding tool calls into the main loop made the file massive, and storing conversation history in flat .json files wasn't scalable. I just released the v1.2.0 update , focusing purely on technical debt and scalability. Here is a breakdown of the refactoring process: 1. The Database Migration (SQLite) Previously, JARVIS saved facts (like user preferences) and chat history in JSON. I ripped that out and implemented a jarvis_memory.db using SQLite. Now, when the LLM triggers the guardar_recuerdo (save memory) tool, it executes a clean SQL insert. Note for dev newbies: When using SQLite in a desktop app, make sure to use sys.exit(0) instead of os._exit() on shutdown to prevent WAL (Write-Ahead Logging) corruption! 2.…