**weweweai/translator** — the interesting part isn't what it does. It's *how* it thinks about doing it. Every AI translation feature you've used this year was cloud-first: your text leaves your device, hits an API, comes back. This tool flips that. The intelligence lives on your machine. Zero round-trips. No SaaS dependency. No usage cap. That's not a limitation — that's an architectural philosophy. We're in peak AI SaaS-ification right now. Every capability is becoming a subscription endpoint. But edge intelligence — agents that run where the data lives — is quietly becoming the more interesting design space. This repo is a clean example of that pattern applied to one of the most common NLP tasks: translation. The agent loop here is as minimal as it gets: 👁 **Perceive** → detect incoming tweet language 🧠 **Decide** → does this match your preference? ⚡ **Act** → translate inline, before it hits your eyes No framework. No orchestration layer. Just the loop. The question I'm forking this to explore: what does this pattern look like generalized? Not just tweets — emails, Slack threads, GitHub issues, PR comments. A local translation agent that wraps any text stream, runs at the edge, and never phones home. The infra for that already exists. The missing piece is the right abstraction. 🌐