Show HN: Plotting New York using a collision dataset https://ift.tt/DHlIkQjM8

Show HN: Plotting New York using a collision dataset https://ift.tt/ZxzMwXETY January 30, 2022 at 03:14AM

Show HN: An offensive security toolkit written in Rust https://ift.tt/bkBQA8edp

Show HN: An offensive security toolkit written in Rust Remote Code Oxidation is a collection of tools that help offensive security professionals quickly adapt payloads to the needs of their engagement. Any and all feedback is welcome! https://bit.ly/3KRPPcz January 29, 2022 at 10:10PM

Show HN: A cross-platform multi-target dotfiles manager https://ift.tt/RxMX2LKE9

Show HN: A cross-platform multi-target dotfiles manager https://bit.ly/3HsDFF3 January 29, 2022 at 09:33PM

Show HN: Troogl – A new way to read the news https://ift.tt/TwiIl9SYj

Show HN: Troogl – A new way to read the news https://bit.ly/3obxbT4 January 29, 2022 at 06:55PM

Show HN: New Feature] we added new breathing exercise https://ift.tt/s3GxXRYn9

Show HN: New Feature] we added new breathing exercise https://getvealth.com/ January 29, 2022 at 05:14PM

Show HN: Faster R-CNN object detector implemented in PyTorch and TensorFlow 2 https://ift.tt/3gnoQYt

Show HN: Faster R-CNN object detector implemented in PyTorch and TensorFlow 2 Fresh and (I think) clean implementations of Faster R-CNN in PyTorch and TensorFlow 2/Keras. I wanted to learn about object detectors and decided to understand and implement a foundational model in the field, Faster R-CNN (elements of which are still used in modern models to this day), using the paper alone. That proved to be more difficult than expected and I had to relent and take a peak at existing implementations to fill in some important gaps. I've documented my struggles and learnings in the README for others to benefit from. I also wanted to solidify my understanding of TensorFlow/Keras and learn PyTorch, so I made sure to implement the model in both. Faster R-CNN is fairly challenging in that it doesn't quite map to Keras tutorial examples. For example, losses are not computed simply as a function of model output and input but rather, some of the training data is actually computed on-the-fly within the model during training and the losses have to explicitly be constructed as part of the graph. Personally, I found the "official" TensorFlow reference implementation ( https://ift.tt/3s2Oirg... ) to be very difficult to follow and I hope this proves to be useful to learners like myself. https://ift.tt/3G4bbzK January 29, 2022 at 12:49AM

Show HN: CraftBox, Run a Minecraft Server on Your Phone https://ift.tt/35npdQg

Show HN: CraftBox, Run a Minecraft Server on Your Phone I want to share my newest Android app with everyone on HN. CraftBox allows you to easily run a Minecraft server on your Android device. It is published in the Play Store, https://ift.tt/3oa6jmG... , and posted on GitHub: https://ift.tt/3IQuqi9 I know I am late to the Minecraft scene. I am a bit old to have been part of the original craze, but I now have a son who loves the game. Now that I have played it with him, I can see the appeal. I am an open source developer and the creator of UserLAnd, https://ift.tt/2pZzVo6 , so when I get excited about something, I am always thinking about how I can get involved and contribute to the community. I read an article on how to run a Minecraft Server on your phone, https://ift.tt/2LSxHlp , but I figured I could do better by not making people go through as many steps. So, that is a goal... make this simple. This is the first public release and there are many things that can be improved. You can see some of the issues I am going to be fixing soon in the GitHub issues. Anyway, please check it out and tell me what you think. Thanks! Corbin https://ift.tt/3IGHUN6 January 29, 2022 at 12:43AM