AI Video Creation Workspace

From topic research to reviewed videos, verified delivery and performance feedback

AI 工作流联网研究人机协同审核CodexChatCutReactTypeScriptCloudflare WorkersD1R2View Project

Videos Validated End to End

Review and Delivery Milestones

Standard Cover Formats

Configured Content Sources

Project Summary

A private workspace connecting multilingual topic sourcing, keyword research, script and video reviews, revisions, production delivery, publication records and performance evidence. Built around human approval and traceable versions, it has been validated through four real video projects. Approved outputs are linked to their review history and verified archives.

Background & Challenge

Research, scripts, source media, editing projects and performance screenshots were scattered across websites, conversations and production tools. Incomplete inputs, unclear execution status and mismatched revisions made it difficult to know what was ready, what required review and which deliverable was final.

My Role

I defined the product requirements, workflow, interface and acceptance criteria from my own video production practice. Through AI-assisted development and hands-on reviews, I iterated on the application and validated it with real projects. I prioritized reliable handoffs, recoverable tasks and verified delivery, with separate approval boundaries for content quality, paid actions and publication.

AI Integration

Driven by topic selection and production tasks, the website maintains original draft versions, review comments, execution statuses, and deliverable references. Codex sessions bridge research and production tools based on authorization, while ChatCut handles media editing and synthesis. Interruption recovery is supported through task queues, execution leases, and checkpoints; current approvals are bound to draft versions and file hashes, with full read-back verification performed after archiving. The frontend is built with React and TypeScript, while services and data storage rely on Cloudflare Workers, D1, and R2. Retrospective and Reflection

Implementation Process

Streamline the personal content creation workflow, establishing the workbench structure, key milestones, and acceptance criteria; build a topic library incorporating Chinese and English information sources alongside screening and curation workflows; integrate keyword research to organize fragmented ideas into reference articles with source citations; design six review checkpoints: source, script, cover, final cut, promotion, and delivery; use real topics to conduct end-to-end integration of personal voiceover, editing, subtitles, background music, and branded outro; complete dual-aspect-ratio covers and promotional assets, upload the final cut, and verify the complete delivery package; connect publishing registration, data collection rules, mobile screenshot uploads, and data retrospectives.

Key Deliverables

Deployed private AI video creation workbench;

Keyword research and material curation module supporting source citations;

Task workflow featuring version binding, human review, and rework tracking;

4 production-ready videos with matching dual-aspect-ratio covers and complete promotional materials;

Downloadable and checksum-verified final asset archives and complete delivery packages;

Portals for publishing registration, mobile screenshot uploads, and performance retrospectives;

Reusable production standards for subtitles, background music, and branded outros

Reflection

Actual production revealed issues such as missing assets, task idling, desynchronization between subtitles and voiceover, and finished videos failing to sync back to the platform. Following iterations, we shifted asset availability checks upstream, bound review workflows to current versions, and verified that delivered files were indeed downloadable. The most valuable takeaway was using real tasks to validate every handoff and codifying human feedback into production guidelines. Playback and engagement metrics still require continuous sample collection, as the effectiveness of changes cannot be judged based on a single data point alone.

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