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Why did Hamd Waseem make Kynetic?
Waseem wanted to create whiteboard-style drawing animations without relying on the online tools he had tried, which he found paywalled or unsuitable for the effect he wanted. He considered Manim, but says its animation style did not match his goal. Instead, he built on handanim, a Python library for hand-drawn animation, and extended it to support the images and movement Kynetic needed.
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The project’s goal was not just to make an animation engine. It was to let people build a project visually without having to write each animation directly in Python, while keeping the rendering pipeline self-hostable.
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The JSON project is the contract
Kynetic separates the graphical editor from the renderer. The editor exports a JSON document; a Pydantic schema validates that document, and the renderer translates its nodes into handanim drawables and transitions before producing the animation.
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That shared format is the boundary between the two components. The editor and renderer can change internally as long as they continue to agree on the project structure. Waseem describes this separation as a key lesson: “Designing the JSON project format gave me that boundary, and meant I could develop the editor and renderer independently.”
Handanim needed two targeted extensions
Waseem added VectorSVG to draw SVG paths with color, using svgelements to parse SVG data. He also added TranslateToPersist, which applies a translation to a drawable’s operations so the object remains in its new position rather than appearing to move only temporarily. He says he contributed these changes upstream.
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How can you make whiteboard animations without writing Python?
The intended workflow is to create the scene in Kynetic’s GUI, export it as JSON, and give that project to the renderer. The JSON schema represents the drawable objects and transitions in a form the Python engine can consume. Python remains part of the implementation, but users do not have to compose every animation by writing Python code themselves.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute- Create the project in the editor. Add the scene elements and animation behavior through the graphical interface.
- Export the project. The editor produces the JSON document that serves as the renderer’s input.
- Render the document. The Docker-packaged Python renderer validates and interprets the project, then uses handanim to produce the animation.
Why package the renderer in Docker?
The renderer depends on both Python packages and operating-system tools, including ffmpeg and pycairo. Waseem describes a container build that installs Python, system dependencies, Poetry, and the application, with main.py as the entry point and Poetry dependency files defining the Python packages.
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Bundling those dependencies in a container is the project’s portability strategy: the user runs a renderer with its expected software environment rather than assembling those pieces by hand. The account does not provide a cross-platform test matrix, so Docker should be understood as the chosen packaging approach, not evidence that every host configuration was tested.
How do optional remote assets work?
Remote asset handling is optional. In the described flow, the frontend asks a Lambda function, reached through API Gateway, for a presigned S3 upload URL. The browser uploads an SVG to S3 and stores an s3:// asset reference in the JSON project. During rendering, the container recognizes remote assets and fetches them before processing the scene.
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For headless use, the renderer can receive API details through environment variables; in interactive use, it can request the necessary input. This keeps the project format usable with remote assets while leaving the rendering step local.
Why keep rendering local instead of running it on AWS?
Waseem considered a cloud-hosted alternative in which an S3 event would start a container on ECS/Fargate to render a job. He chose not to build that into the described pipeline. Local Docker rendering better fit his bring-your-own-cloud approach and avoided turning Kynetic into a hosted rendering service with recurring infrastructure costs.
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| Design choice | Implemented approach | Considered alternative |
|---|---|---|
| Where rendering runs | In a Docker container run locally by the user | On ECS/Fargate in the cloud |
| How a render starts | User runs the renderer, which fetches referenced remote assets when needed | An S3 event starts a rendering container |
| Infrastructure model | User-managed BYOC setup; AWS asset services are optional | Hosted rendering infrastructure with ongoing operating costs |
The ECS/Fargate flow was a design alternative, not a second deployed or tested Kynetic mode.
What did the CORS problem reveal?
The browser’s upload flow exposed a mismatch between frontend requirements and the renderer’s assumptions. Browsers may send an OPTIONS preflight before an upload request, and missing CORS headers on error responses also caused uploads to fail.
Waseem first changed the existing URL-generating functions to address the browser behavior, but those changes broke what the renderer expected. He reverted them, added separate frontend-specific URL functions, and updated the Next.js frontend to use those routes. The fix kept browser-specific behavior at the frontend/API boundary instead of changing shared routes in a way that disrupted rendering.
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What deployment settings did Waseem report?
For API throttling, Waseem says he chose a default of 10 requests per second with a burst of 2 for a deployment intended for one user or a few. That is his reported configuration, not a general AWS limit, a measured capacity result, or an independent recommendation for other deployments.
The account does not provide performance, rendering-time, cost, adoption, or reliability measurements. It also does not independently establish the current availability or maintenance status of the project, so the architecture is best read as Waseem’s account of what he built rather than a current product audit.
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