Rachel Nabors
Rachel Nabors is a developer-experience and developer-relations practitioner at Arize AI, focused on AI evaluation, observability, local LLM workflows, and bringing web developers into applied AI. They previously worked across the web platform and developer ecosystem, including React, MDN/Mozilla, Meta, AWS, Microsoft Edge, and W3C-related work.
Small Local Models Can Replace Frontier Calls When Product Evals Prove Fit
RL Nabors of Arize argues that teams should stop treating frontier-model calls as the default for production AI features. Her case is to prototype with the strongest model when needed, then use golden datasets, capability evals, and trace-based measurements to work down to the smallest local or task-specific model that meets the product’s own bars for accuracy, latency, cost, privacy, and reliability. In her Mima summarization example, that process moved a Claude-backed feature to an on-device Llama 3.2 3B configuration, with evals becoming the guardrail for future changes.
Agent Interfaces Are Moving From Chat to Web-Native Surfaces
Rachel Nabors argues that chat should be treated as a transitional interface for agents, not their final form. Using her rebuilt Rachel the Great web comic archive as the example, she shows how MCP apps can render HTML, CSS and JavaScript inside Claude as a working comic reader, while WebMCP can expose a site’s existing functions directly to browser agents. Her case is that the web platform already provides the “infinite canvas” for agent software; the task is to let agents inherit it rather than confining them to text conversations.