Weaver

Weaver is a local-first AI translation workspace built for translating Japanese light novels at scale. It combines project management, translation workflows, glossary systems, translation memory, and multi-provider AI orchestration into a single environment.

The Problem

Translating long-form content such as light novels is difficult to manage with generic AI chat tools. Context becomes fragmented, terminology drifts over time, and maintaining consistency across hundreds of chapters quickly becomes a manual process.

Most workflows end up relying on scattered prompts, spreadsheets, notes, and repeated corrections.

What I Built

I built Weaver to centralize the entire translation workflow.

The system manages novels, volumes, and chapters while coordinating AI-powered translation providers, glossary injection, character databases, translation memory, batch processing, and export pipelines. Instead of treating translation as isolated prompts, Weaver treats it as a structured, long-running project.

The architecture is designed around reusable workflows where translations can be reviewed, refined, retranslated, and exported without losing project context.

What Shipped

A working translation platform with support for:

• Novel and chapter management
• AI-assisted translation workflows
• Multiple translation providers
• Translation memory reuse
• Glossary and character databases
• Batch translation operations
• Export pipelines for publication-ready outputs

The project evolved through multiple development sprints focused on reliability, workflow efficiency, and long-form translation consistency.

What I Learned

The hardest challenge was not generating translations — modern models can already do that well. The real challenge was building a workflow that keeps context, terminology, and project state consistent across an entire book.

For long-form translation, workflow design matters just as much as model quality.