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Source channel @githubtrending · Post #14627 · Apr 24

#jupyter_notebook DINOv2 is a powerful AI model from Meta AI that learns to understand images without needing labeled data, using self-supervised learning. It was trained on 142 million images and creates strong visual features that work well for many tasks like image classification, depth estimation, and segmentation without extra fine-tuning. You can use its pretrained models easily with simple classifiers, saving time and effort. DINOv2 is efficient, scalable, and performs better than many other models, making it great for building versatile computer vision applications quickly and accurately. It’s open-source and ready to use with PyTorch. https://github.com/facebookresearch/dinov2

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Libreware

@libreware · Post #1554 · 02/22/2026, 03:26 PM

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The Internet, Reinvented. In this video, I build a #Reticulum#RNode and prove that completely different radios — #LoRa and Wi-Fi — can communicate through a hardware-agnostic networking stack. Reticulum routes traffic above the radio layer, automatically bridging dissimilar frequencies, interfaces, and modulation types. I then run it over Wi-Fi HaLow Haven nodes to create a long-range, encrypted IP #mesh with no traditional infrastructure. Finally, I push it further by running #ATAK across the network, demonstrating a fully open-source, decentralized communication stack in action. Checkout https://rmap.world/ You can install rnode software on your esp32/nrf52 based meshtastic/meshcore hardware

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#reticulum #rnode #lora #mesh #atak