KD Subs

KD Subs is a self-reported tool for DaVinci Resolve that automates animated subtitle creation with word-timed animations and editable timing, built using AI assistance (Codex) and Fusion scripting. The author describes it as solving a personal workflow problem in motion design, where repetitive technical work was reduced through automation. It operates within DaVinci Resolve without requiring users to leave the editor, and uses a structured development process involving Codex for implementation and Git for version control.

The project is described as functional but not yet commercially deployed or tested with external users beyond the creator's own workflow. There is no evidence of revenue, customers, or market traction. The tool is positioned as an internal productivity enhancement rather than a commercial product.

The description states that KD Subs:

• Turns existing DaVinci Resolve subtitle tracks into editable, word-timed animated motion graphics.
• Reads subtitle clips, divides content into words and display lines, calculates timing, generates TextPlus output, and synchronizes active-word highlighting with speech.
• Operates inside DaVinci Resolve without requiring users to switch applications or leave the editor.
• Uses a system that separates processes into stages: Subtitle Track → Speech Words → Display Lines → Word Timing → Block Timing → Highlight Map → TextPlus Output.

It is described as a professional Fusion template for motion design, not a standalone application. The author emphasizes its focus on maintaining editability and control over visual elements while automating timing logic.

Evidence The write-up details how the tool works technically within DaVinci Resolve using Fusion nodes, Lua scripting, expressions, and TextPlus rendering.

Inference The product appears to be a plugin or template for DaVinci Resolve that integrates with its Fusion environment.

The author positions KD Subs as:

• A solution to repetitive technical work in subtitle animation.
• An alternative to manual editing or switching tools like CapCut.
• A way to keep the workflow inside DaVinci Resolve while preserving creative control.
• A tool that automates timing and synchronization across multiple layers of animation.

It claims to:

• Reduce time spent on repetitive tasks.
• Maintain visual flexibility and editability.
• Keep users within their preferred editing environment.
• Combine automation with meaningful user control.

The evolution of the claim appears to be from a personal productivity tool to a potential broader toolkit for subtitle design in DaVinci Resolve, based on the author’s stated future plans.

Evidence The write-up describes how the tool evolved from an idea into a working solution and outlines future directions.

Inference The positioning reflects a shift from solving one's own workflow issues to building a reusable creative system.

The description indicates that KD Subs targets:

• Motion designers working in DaVinci Resolve.
• Professionals who create animated subtitles for short-form videos.
• Users who want to stay within DaVinci Resolve while doing animation work.

It is implied that the primary user is someone already familiar with Fusion and DaVinci Resolve, likely a professional editor or designer.

Evidence The write-up describes the author's own use case as a motion designer in DaVinci Resolve.

Inference The ICP seems to be advanced users of DaVinci Resolve who require both automation and control over visual output.

There is no evidence provided about pricing, monetization, or business model. The project is described as self-developed by one person and submitted to a hackathon.

Evidence No mention of sales, subscriptions, licensing, or revenue streams.

Inference The tool appears to be non-commercial at this stage, possibly intended for personal or internal use only.

The project uses:

• Codex as an engineering partner.
• Lua scripting and Fusion expressions.
• Git for version control.
• TextPlus rendering within DaVinci Resolve.
• GPT-5.6 for deeper reasoning and architectural review.

Development workflow includes:

• Defining features in notes.
• Asking Codex to inspect architecture and propose plans.
• Reviewing and correcting assumptions.
• Letting Codex implement approved solutions.
• Manual testing inside DaVinci Resolve.

The system separates the process into multiple interconnected stages, making it modular and easier to test and extend.

Evidence The write-up details the technical stack and development process.

Inference This suggests a structured, iterative approach to building complex Fusion templates with AI assistance.

There is no evidence of:

• Revenue.
• Customers or user base.
• Market adoption.
• Product usage beyond the creator’s own workflow.
• Public testing or feedback from others.

The tool is described as functional but not yet deployed for general use. It is presented as a working prototype, not a commercial product.

Evidence The author states it solves a real problem in their workflow and has been tested internally.

Inference The maturity level is early-stage development with no external validation or traction.

The description does not provide information about competitors or similar tools. It implies that existing alternatives include:

• Manual editing of subtitles.
• Moving projects to other applications like CapCut.

It is unclear whether there are other tools in the market offering similar functionality within DaVinci Resolve.

Evidence No mention of competing products or market analysis.

Inference The competitive landscape is unknown, but the tool addresses a gap in automation for subtitle animation inside DaVinci Resolve.

Key risks include:

• Lack of external validation or user feedback.
• Dependency on a single developer (team size: 1).
• Unclear scalability beyond one person’s workflow.
• No evidence of performance optimization or testing with varied content types.
• Limited documentation or packaging for distribution.

Red flags:

• No mention of formal release, installation instructions, or distribution strategy.
• No indication of how the tool might be maintained or updated post-hackathon.
• The reliance on Codex and GPT-5.6 raises questions about reproducibility and long-term viability if those tools change.

Evidence The write-up focuses on internal development and personal use cases.

Inference The risk is high due to lack of external testing, scalability concerns, and dependency on a single individual.

1. What specific problems in your workflow did you encounter before building this tool?
2. How do you plan to test the tool with other users or editors beyond yourself?
3. Are there any known limitations or edge cases that affect performance or usability?
4. What are the key assumptions about user behavior or technical constraints that could impact adoption?
5. Do you have a roadmap for expanding functionality, and how will you validate new features?
6. How do you intend to distribute or package the tool for others to use?
7. What level of support or maintenance can users expect from this project?