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Kynetic separates animation editing from rendering: users create a project in a graphical editor, export it as JSON, and run that project through a Docker-packaged renderer built on the Python library handanim. Optional AWS services handle remote assets, but rendering stays local in the architecture Hamd Waseem describes. That design grew out of a practical goal: make whiteboard animations without relying on unsuitable paid web tools or requiring every user to write animation code.
Why did Waseem make Kynetic?
Waseem wanted to create videos in which a hand appears to draw images on a whiteboard. He found the free online options he tried either paywalled or unsuitable for the effect he wanted. He considered Manim, but says its animation style did not match his intended whiteboard look.
He chose handanim, a Python library for hand-drawn animation, as the rendering foundation. Rather than make users author animations directly in Python, he built a graphical editor around a project format the renderer could consume.
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How does Kynetic turn an edit into an animation?
The workflow has two parts: an editor that describes a scene and a renderer that turns that description into a video. The editor exports a JSON project; a Pydantic schema validates its structure, and the renderer maps project nodes to handanim drawables and transitions.
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- Create the scene: Arrange animation content in the GUI editor.
- Export the project: Save the scene as JSON in the shared project format.
- Render it: Run the Python renderer, which reads the nodes, constructs the scene objects, and produces the animation.
The JSON format is the contract between the two components. The editor and renderer can change internally so long as each continues to honor that document structure.
What did he add to handanim?
Waseem reports extending handanim in two targeted ways to better match the project’s drawing and movement needs.
- VectorSVG: Draws SVG paths with color. The implementation uses svgelements to parse SVG data.
- TranslateToPersist: Moves an object while keeping it in its translated position, rather than leaving it at its original coordinates after the movement.
Waseem says he contributed these changes upstream. That is his account of the work; it does not establish the current status of the library or its releases.
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Why package the renderer in Docker?
The renderer depends on both Python packages and operating-system tools, including ffmpeg and pycairo. Waseem describes a Docker build that installs Python, system dependencies, Poetry, and the application entry point. The renderer has a main.py entry point and Poetry dependency files.
Packaging these pieces together is intended to make setup more consistent across environments. The account does not provide a cross-platform test matrix, so Docker should be understood as the chosen portability approach, not proof that every operating system or machine configuration was tested.
How do remote assets fit into a local-rendering design?
Remote assets are optional. In the described AWS flow, the frontend asks a Lambda function behind API Gateway for a presigned S3 upload URL, uploads an SVG to S3, and records an s3:// asset reference in the JSON project. At render time, the container detects remote assets and fetches them.
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For headless runs, the renderer can receive API details through environment variables; the author also describes interactive input as an alternative. The remote asset path therefore supplements the local renderer rather than moving the rendering job itself into AWS.
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Waseem considered a cloud-native alternative in which an S3 event would start a container on ECS or Fargate. He retained user-invoked local rendering instead, to avoid turning a bring-your-own-cloud project into a hosted rendering service with ongoing infrastructure costs.
| Decision point | Kynetic architecture described by Waseem | Considered alternative |
|---|---|---|
| Where rendering runs | Locally in a Docker container | In a cloud-hosted ECS/Fargate container |
| How a job begins | The user invokes rendering; the renderer fetches remote assets as needed | An S3 event triggers the container |
| Infrastructure responsibility | Users manage their own BYOC setup | A hosted rendering service carries ongoing infrastructure costs |
The cloud-hosted flow is a counterfactual, not a second deployed or tested Kynetic mode. The tradeoff is architectural: local rendering keeps compute under the user’s control, while a managed job pipeline would require more cloud orchestration and continued infrastructure spending.
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What did a CORS failure reveal?
The browser upload flow exposed a mismatch between frontend behavior and assumptions in the renderer-facing API. Browsers may send an OPTIONS preflight before an upload request, and error responses without CORS headers can also cause browser requests to fail.
Waseem says his first fix changed the original URL-generating functions, which addressed browser behavior but broke what the renderer expected. He reverted that change, added separate frontend-specific URL-generation functions, and updated the Next.js frontend to call those routes. This kept browser-specific CORS handling from changing the API behavior shared with the renderer.
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What API throttle did he configure?
Waseem reports choosing an API throttle of 10 requests per second with a burst of 2, expecting the deployment to serve one user or a few. This is his configuration for that anticipated use, not a general AWS service limit, a measured capacity result, or an independent recommendation.
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What did Waseem take away from building Kynetic?
The project’s central lesson is the value of an explicit boundary between components. Waseem writes: “Designing the JSON project format gave me that boundary, and meant I could develop the editor and renderer independently.” The schema and JSON document let the GUI and renderer evolve separately while preserving a shared understanding of a project.
The retrospective describes implementation choices rather than an independently verified current product audit. It does not establish present-day repository activity, release availability, rendering performance, operating costs, adoption, or reliability.
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