Ototo Code Exploration Offloader

Offload codebase exploration and token-heavy tasks to a local small model to save context and cost

Problem

Using Claude and similar models for code exploration burns tokens on search, file reading, and summarization, which is expensive and pollutes the main context; developers want to delegate mechanical work to cheap or local small models.

Solution

A dev tool/CLI proxy layer: accepts exploration-type prompts (find functions, summarize modules, list dependencies), routes them to a local or cheap small model, and returns condensed results to the main model. Configurable routing rules, result caching, and a clean main context.

Users

Developers heavily using AI coding assistants, cost-sensitive indie developers and small teams

MVP

Define routable exploration tasks (grep, file summaries, dependency lists); configure local models (e.g., Ollama) or cheap APIs as backends; provide CLI and simple UI to show results; cache results for reuse; integrate with existing editor workflows (copy/paste or MCP).

Revenue

Open-source core + paid Pro (team rule sharing, usage analytics, cloud small-model proxy); or tiered subscriptions based on tokens saved.

Why now

AI coding assistants are mainstream but token cost and context management remain pain points; small local models (Llama, Qwen, etc.) are now good enough to make offloading viable.