Starforge Kernel

An End-to-End AI Comic-Drama Production Operating System

AI AgentCodex SDKDeepSeek多模态生成ReactTypeScriptHonoSQLiteFFmpegPlaywright工作流编排浏览器自动化

Episodes Managed

456

Storyboard Shots

2,053

AI Jobs Orchestrated

Project Summary

Starforge Kernel is an end-to-end production operating system for AI comic dramas. It connects script breakdown, consistency asset management, keyframe and video generation, non-destructive assembly, subtitles, quality control, and platform publishing through resumable and auditable episode-level workflows. Codex, DeepSeek, external AI APIs, and browser automation operate as interchangeable execution channels.

Background & Challenge

AI comic-drama production was fragmented across disconnected tools. Scripts, prompts, reference images, generated shots, editing, subtitles, and publishing required repeated manual transfer and verification. As production scaled, character drift, missing references, duplicated jobs, lost progress, and inconsistent final assemblies became major operational risks.

My Role

I served as the product architect and full-stack AI workflow developer. Instead of building another single-purpose generation interface, I designed a persistent production system around a unified drama–episode–shot–asset–assembly–distribution model. My strategy combined provider-agnostic AI routing, resumable state machines, consistency controls, human approval gates, and production-grade browser automation.

AI Integration

The architecture combines domain modeling, provider adapters, persistent job orchestration, consistency controls, and post-production delivery. Provider Profiles route work across Codex, DeepSeek, image and video APIs, Vidu, and Jimeng browser automation. Approved asset dependencies, reference packing, identity QC, non-destructive EDL assembly, FFmpeg rendering, subtitles, and distribution checkpoints keep the workflow reproducible and safe.

Implementation Process

Phase 1: Build the core drama, episode, shot, job, and provider data model

Phase 2: Establish consistency assets, approvals, dependencies, and audit history

Phase 3: Connect script breakdown, asset mounting, prompts, keyframes, and video generation

Phase 4: Add persistent episode pipelines, task operations, concurrency, and recovery

Phase 5: Deliver non-destructive assembly, timeline editing, AI direction, QC, and subtitles

Phase 6: Isolate multiple projects, engine preferences, and physical output directories

Phase 7: Launch the distribution center with Kuaishou cover, metadata, scheduling, and publishing automation

Key Deliverables

Starforge Kernel AI comic-drama production workspace

Multi-project episode and storyboard management

Character, location, and prop consistency asset library

Multi-provider AI routing and adapter architecture

Persistent episode-level automation pipeline

Background task center and concurrent execution queues

Non-destructive assembly and subtitle rendering

AI assembly director and production quality controls

Dual-thread Jimeng browser video automation

Kuaishou publishing workflow with human confirmation

Reflection

The project changed my view of generative quality: model choice and prompting are only part of the outcome. Complete upstream assets, locked character identity, correct context, and reliable reference delivery matter just as much. I also learned that real automation must be a recoverable, observable, and re-entrant state machine rather than a chain of buttons. Paid actions, ambiguous results, and public publishing still require explicit safeguards and human checkpoints.

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