#python#android#android_emulator#google_apps#kernelsu#magisk#magiskonwsa#magiskonwsalocal#subsystem#windows#windows_10#windows_11#windows_subsystem_android#windows_subsystem_for_android#windows10#windowssubsystemforandroid#wsa#wsa_root#wsa_with_gapps_and_magisk#wsapatch
Windows Subsystem for Android (WSA) support ended on March 5, 2025, and the Amazon Appstore was removed from the Microsoft Store, but you can still manually install and use WSA on Windows 10 or 11 via unofficial builds like WSABuilds from GitHub. These builds include options with Google Play Services and root access (Magisk). If you face issues with apps crashing or not starting after recent Windows updates, try using older or "NoGApps" builds as workarounds. Backing up your data before uninstalling or updating WSA is recommended. This lets you keep running Android apps on Windows despite official support ending.
https://github.com/MustardChef/WSABuilds
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What’s Really Going On in Machine Learning? Some Minimal Models—Stephen Wolfram Writings
https://writings.stephenwolfram.com/2024/08/whats-really-going-on-in-machine-learning-some-minimal-models/
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Meta's second version of segment anything.
https://github.com/facebookresearch/segment-anything-2
They have a nice demo:
https://sam2.metademolab.com/
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I was searching for a tool to visualize computational graphs and ran into this preprint. The hierarchical visualization idea is quite nice.
https://arxiv.org/abs/2212.10774
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Like a dictionary
Kunc, Vladim’ir, and Jivr’i Kl’ema. 2024. “Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networks.” arXiv [Cs.LG], February. http://arxiv.org/abs/2402.09092.
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I got interested in satellite data last year and played with it a bit. It's fantastic. The spatiotemporal nature of it brings up a lot of interesting questions.
Then I saw this paper today:
Rolf, Esther, Konstantin Klemmer, Caleb Robinson, and Hannah Kerner. 2024. “Mission Critical -- Satellite Data Is a Distinct Modality in Machine Learning.” arXiv [Cs.LG], February. http://arxiv.org/abs/2402.01444.
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Jelassi S, Brandfonbrener D, Kakade SM, Malach E. Repeat after me: Transformers are better than state space models at copying. arXiv [cs.LG]. 2024. Available: http://arxiv.org/abs/2402.01032
Not surprising at all when you have direct access to a long context. But hey, look at this title.