Alex Patterson is working on @CodingCatDev

Vector similarity search often returns half-broken chunks when AI agents need exact rules. With Sanity Knowledge Bases and Context MCP, agents read the structured outline first and fetch complete Markdown entries by path. Breakdown in the reply 👇

Context MCP: Give AI Agents Precise Knowledge Base Access. Connect AI agents to a knowledge base with Context MCP. Load the outline, choose exact entry paths, and read full Markdown entries, without relying on similarity search to find the right chunk.

Instead of letting an agent guess which vector chunk matters, start with two things in the config: a sharp dataset purpose and a focused GROQ projection. That produces a clean outline for direct entry-path reads via Context MCP. See it in the reply 👇

Sending sensitive patient data to a cloud LLM API just to parse a voice request is a massive HIPAA compliance headache. With local Speech-to-Text and a 12B Gemma model, you can execute tool calls 100% offline on-device. Breakdown in the reply 👇

Offline On-Device AI Agents with Gemma & Local Tool Calls (feat. @howdevelop) How do you run an agentic healthcare workflow without sending private patient data to the cloud? Run local Speech-to-Text + a 12B Gemma model on-device to execute pharmacy tool calls 100% offline.