Generate browser header, anti header request
info: fake chrome, firefox, opera browser header anti header Features more header params more request method Installation Usage
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Linting support for python using the pylint library
A Visual Studio Code extension with support for the pylint linter. The extension ships with pylint=2.12.2. Settings Settings Default Description python.pylintArgs [] Custom arguments passed to pylint. E.g “python.pylintArgs” = [“–rcfile=”] python.pylintSeverity { “convention”: “Information”, “error”: “Error”, “fatal”: “Error”, “refactor”: “Hint”, “warning”: “Warning”, “info”: “Information” } Controls mapping of severity from pylint to VS Code severity when displaying in the problems window. You can override specific pylint error codes { “convention”: “Information”, “error”: “Error”, “fatal”: “Error”, “refactor”: “Hint”, “warning”: “Warning”, […]
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i3 dotfiles and installation scripts
Installation currently auto installation is only supported for arch linux and arch based distros.clone the repo git clone https://github.com/Anomic-cr/i3_dotfiles.git And run the installation script and you should be good to go Manual Installation Install the dependencies and copy the config files Dependencies i3-gaps, sddm, betterlockscreen, i3status-git, i3blocks-git, alacritty, rofi-git, neofetch-git, polybar-git, dunst-git, feh-git, nitrogen, picom, lxappearance, xorg, xorg-xinit, xorg-server, qt5, oh-my-zsh, zsh Optional Dependencies
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A free alternative of the tool to check invites (with min/max members number) by genefit
đź’° a free alternative of the tool by genefit Preview Warning My tool is probably more slow than genefit tool (but imagine paying 5$ for a checker lol) I never tried the genefit tool but I know how it works so I did this Maybe original Checky is writen in Golang, but mine is in Python Features Checks Invite name, and members Store the invites if there are corrects Proxies support Todo Built with Requirements Installation # Clone the repo: […]
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A Python bot which can compare and assist in predicting future crypto prices
A cryptocurrency bot which can assist you in predicting future crypto prices through comparing previous crypto prices with actual prices. The bot uses the method of Death Cross vs Golden Cross, a reliable method for solid predictions of prices, to make a plot based on previously predicted data. Image: Preview of the graph Table of Contents Features Interactive graph Allows user to input to customize the following in the chart – currency, cryptocurrency, time period and time interval Various built-in […]
Read moreWoodyll – A python script allowing for fast tpll and point marking
v1.1 woodyll is a python script allowing for fast tpll and point marking. woodyll opens your minecraft, teleports you to coordinates from your clipboard, marks down the point and switches back to map. Windows installation Verify if your PC has python installed by opening cmd and type py –version. If your PC has python installed, proceed to step 2. If your PC doesn’t have python installed, download it from python.org. Download woodyll by running git clone https://github.com/woodyamazing/woodyll.git in cmd or […]
Read moreLAFITE: Towards Language-Free Training for Text-to-Image Generation
Code for paper LAFITE: Towards Language-Free Training for Text-to-Image Generation (CVPR 2022) Update more details later. Requirements The implementation is based on stylegan2-ada-pytorch and CLIP, the required packages can be found in the links. Preparing Datasets Example: python dataset_tool.py –source=./path_to_some_dataset/ –dest=./datasets/some_dataset.zip –width=256 –height=256 –transform=center-crop the files at ./path_to_some_dataset/ should be like: ./path_to_some_dataset/   ├ 1.png   ├ 1.txt   ├ 2.png   ├ 2.txt   ├ … We provide links to several commonly used datasets that we have already processed (with CLIP-ViT/B-32): MS-COCO Training Set […]
Read moreBackpropagation Applied to Handwritten Zip Code Recognition
This code tries to reproduce the 1989 Yann LeCun et al. paper: Backpropagation Applied to Handwritten Zip Code Recognition. To my knowledge this is the earliest real-world application of a neural net trained with backpropagation (now 33 years ago). run Since we don’t have the exact dataset that was used in the paper, we take MNIST and randomly pick examples from it to generate an approximation of the dataset, which contains only 7291 training and 2007 testing digits, only of […]
Read moreInvariant and Equivariant Graph Networks
A PyTorch implementation of The ICLR 2019 paper “Invariant and Equivariant Graph Networks” by Haggai Maron, Heli Ben-Hamu, Nadav Shamir and Yaron Lipmanhttps://openreview.net/forum?id=Syx72jC9tm. The official TensorFlow implementation is at https://github.com/Haggaim/InvariantGraphNetworks Data Data should be downloaded from: https://www.dropbox.com/s/vjd6wy5nemg2gh6/benchmark_graphs.zip?dl=0.Run the following commands in order to unzip the data and put its proper path. mkdir data unzip benchmark_graphs.zip -d data Prerequisites Python3 PyTorch 1.5.0 Additional modules: numpy, pandas, matplotlib TensorFlow is not neccessary except if you want to run the tests (comparisons) between […]
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