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docs/OffGridSchedula — In-Depth Technical Talk Track.md

@@ -4,7 +4,7 @@
 > 📸 **Screenshot suggestion (hero):** the landing/Home hero with the tagline and the badge row (🔒 100% local · 🦙 llama.cpp · 🎯 Fine-tuned · 🪶 ≤ 32B), establishing the "small, local, fine-tuned" thesis up front.
 
 ## [Opener — ~20s]
-OffGridSchedula reads a messy group chat and produces calendar events — entirely on a ~4-billion-parameter model running locally, with no cloud AI in the loop. What I want to show you isn't just *what* each page does, but the engineering decisions that make a small model behave reliably. The recurring theme: **use the LLM only for the fuzzy natural-language-to-structure step, and make everything that can be deterministic, deterministic.**
+OffGridSchedula reads a messy group chat and produces calendar events — entirely on small, locally-served models, with no cloud AI in the loop. It runs on two specialists working together: a fine-tuned ~4-billion-parameter Gemma E4B that turns messy language into structure, and **OpenBMB's MiniCPM** — the agentic planner that reads the thread, decides which tool to call, and drives the app's own functions over the Model Context Protocol. What I want to show you isn't just *what* each page does, but the engineering decisions that make small models behave reliably. The recurring theme: **use the LLM only for the fuzzy natural-language-to-structure step, and make everything that can be deterministic, deterministic.**
 
 ## [The model-type decision — why decoders, not encoders — ~1:15]
 > 📸 **Screenshot suggestion:** a simple two-box slide — *encoder (BERT): classify · tag · embed* on the left, *decoder (GPT-style): generate · reason · orchestrate* on the right — with the app's `ActionPlan` JSON landing under the decoder side. If you have the architecture inset (the two local llama-servers, gemma-cal + MiniCPM), show it here to preview the two-decoder design.