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Source channel @githubtrending · Post #15017 · Aug 1

#typescript#editor#frontend#game_development#game_engine#gamedev#nodejs#playcanvas#typescript#ui The PlayCanvas Editor is a powerful, browser-based tool for creating 3D WebGL, WebGPU, and WebXR apps with real-time collaboration, live editing, and no compile time, letting you build and test projects quickly on any device. It supports advanced graphics features like glTF 2.0, clustered lighting, and WebXR for AR/VR experiences. You can also set up a local development environment to customize or extend the editor using open-source libraries. This means you can develop interactive 3D content faster, collaborate easily, and test directly on mobile or desktop, improving your workflow and project quality[1][2][3][4]. https://github.com/playcanvas/editor

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djangoproject

@djangoproject · Post #274 · 03/18/2017, 01:48 AM

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https://github.com/riga/tfdeploy Google's TensorFlow framework is taking off big-time now that it's at a full 1.0 release. One common question about it: How can I make use of the models I train in TensorFlow without using TensorFlow itself? #Tfdeploy is a partial answer to that question. It exports a trained TensorFlow model to "a simple #NumPy-based callable," meaning the model can be used in Python with Tfdeploy and the the NumPy math-and-stats library as the only dependencies. Most of the operations you can perform in TensorFlow can also be performed in Tfdeploy, and you can extend the behaviors of the library by way of standard Python metaphors (such as overloading a class). Now the bad news: Tfdeploy doesn't support GPU acceleration, if only because NumPy doesn't do that. Tfdeploy's creator suggests using the gNumPy project as a possible replacement. #Machine_learning

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