Telegram

A telegram bot does not allow channels to send messages to the telegram supergroup

Getting started $ git clone https://github.com/AbhijithNT/GroupChannelHandler.git $ cd ChannelMessageHandler Virtual Environment Optional $ pip install virtualenv $ virtualenv venv $ source venv/bin/activate $ pip install -r requirements.txt $ python bot.py Require environment variables Follow the links BOT_TOKEN BotFather Heroku Deploy Built With Contributing Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us. Authors See also the list of contributors who participated in this project. Telegram Channel Join the News channel. […]

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Chess

j-chess implementation in python

This repository aims to be a starting point for implementing a chess ai for the j-chess-server in python. To start, you can copy this repository and add an Ai that extends from BaseAi and call the client with it and you should be ready to go. To regenerate the classes from the xsd, call xsdata .xsdjChessMessage.xsd –package j-chess-generated GitHub View Github    

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Assistant

Used python functional programming to make this Ai assistant

I have used python functional programming to make this Ai assistant. we have seen in our daily life goggle assistant siri Alexa , I was pretty much interested to know how this things are working . So I had worked on this project to learn how they are working . I want to make more personalized ai assistant, so I had created this one. pyttsx3 (python text to speach): to convert text in voice ; datetime: for get the info […]

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Ansible

This collection is to provide an easier way to interact with Juniper

Overview The goal of this collection is to provide an easier way to interact with Juniper’s Apstra solution. While nothing will stop you from using the built-in module, you may find that working with pre-packaged modules can help simplify the development of your playbook, or it may just be easier to support as a team. 📋 Ansible version compatibility There are significant changes to Ansible within version 3.x, and while those changes get worked out we will continue to test […]

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Machine Learning

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

ONNX Runtime is a cross-platform inference and training machine-learning accelerator. ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more → ONNX Runtime training can accelerate the […]

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Binary

A powerful parser generator for reading, processing, executing, or translating structured text or binary files

Build status ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files. It’s widely used to build languages, tools, and frameworks. From a grammar, ANTLR generates a parser that can build parse trees and also generates a listener interface (or visitor) that makes it easy to respond to the recognition of phrases of interest. Authors and major contributors

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Document

A document-focused, decluttered mode of JupyterLab that uses activity-based design

Clarity mode is a single-notebook interface built with existing JupyterLab components. To install: Clone this repository Ensure you have installed jupyter-server (pip install jupyter-server) Run pip install -e . npm install npm run build jupyter clarity In the URL, enter /clarity/path + the path to a notebook, e.g. localhost:8888/clarity/path/mynotebook.ipynb GitHub View Github    

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