Mavekepeter

Mavekepeter's Stars

• Swiftpair/Python-Secure-Socket-Chat

  AUTHOR: ALMODAD MUTINDA - MOI UNIVERSITY - Chat applications today are readily available and very useful in conversing with people that might be close by or far away. Internet chat services provide the convenience of conversing with people in real time. These services provide a host of possibilities for work, school, telecommunication, etc. Unfortunately, most of these widely available chat services do not provide adequate protection/privacy of what is being sent through the chat servers. The objective of this project is to build a secure, cross-platform socket based chat application utilizing Diffie-Hellman key exchange to send secure chat messages across the internet. This application uses the client-server architecture. This application was developed using Python programming language. The server was developed as a console based application while the client was developed as a both console and GUI based application. The server is implemented as a singleton class. The main server thread opens a server socket on the local inet address and a specified port. It then waits for clients to connect to it. When a client connects to the server, it creates a separate thread as well as a User object dedicated to that client. The server maintains a hash map of User objects associated with the clients connected to it hashed against the user name. The thread dedicated to the client opens a buffered stream to read input messages from the client and a print writer stream to send messages to the client. The Client is implemented using two threads, one for incoming messages and another for outgoing messages. The main thread opens the socket and connects to the server. It then opens an input buffered stream and print writer for incoming and outgoing messages respectively. A shared secret is important between two parties who may not have ever communicated previously, so that they can encrypt their communications. As such, Diffie-Hellman Key Exchange (DHKE) is used before any two clients can communicate.

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• Mavekepeter/counter-app-in-react-and-typescript

  Language:TypeScript1

• adrianhajdin/healthcare

  Build a healthcare platform that streamlines patient registration, appointment scheduling, and medical records, and learn to implement complex forms and SMS notifications.

  Language:TypeScript1.7k403

• Mavekepeter/chatgptclone

  Language:Python1

• Mavekepeter/banking

  Language:TypeScript1

• Mavekepeter/flexdisplay

  Language:HTML1

• Killercavin/Mentor

  A PHP Laravel clone and modified version of https://bootstrapmade.com/mentor-free-education-bootstrap-theme/ template

  Language:Blade1

• ente-io/ente

  Fully open source, End to End Encrypted alternative to Google Photos and Apple Photos

  Language:Dart13.5k715

• Simatwa/Made-With-ML

  Learn how to design, develop, deploy and iterate on production-grade ML applications.

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• bregman-arie/devops-exercises

  Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions

  Language:Python65.7k14.6k

• microsoft/ML-For-Beginners

  12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

  Language:HTML68.9k14.2k

• fastapi/fastapi

  FastAPI framework, high performance, easy to learn, fast to code, ready for production

  Language:Python75.1k6.3k

• opsdisk/metagoofil

  Search Google and download specific file types

  Language:Python40083

• martinvonz/jj

  A Git-compatible VCS that is both simple and powerful

  Language:Rust8.1k265

• Simatwa/pyftp

  Extremely fast and scalable Python FTP server based on PYFTPDLIB

  Language:Makefile1

• GRACENGARI/GRACENGARI

  Config files for my GitHub profile.

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• GRACENGARI/California-housing-datavisualization

  Housing datasets can be collected from a variety of sources, including government agencies, real estate listings, and private companies that specialize in collecting housing data. The data may be in various formats, such as spreadsheets or databases, and may include both numerical and categorical variables.

  Language:Jupyter Notebook1

• GRACENGARI/machine-learning-model

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• GRACENGARI/Demorepo

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• GRACENGARI/code1

  ANALYSIS OF DIABETES USING LINEAR REGRESSION

  Language:Jupyter Notebook1

• GRACENGARI/code

  neural network creation using tensor flow

  Language:Jupyter Notebook1

• GRACENGARI/CODE2

  MACHINE LEARNING MODELS

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• GRACENGARI/REGRESSION-ANALYSIS

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• GRACENGARI/INSURANCE-ANALYSIS

  Language:Jupyter Notebook1

• GRACENGARI/TECH-4-DEV.IPYNB

  Language:Jupyter Notebook1

• GRACENGARI/AWS-REPO

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• GRACENGARI/IRIS-FLOWER-SVM

  Language:Jupyter Notebook1

• GRACENGARI/responsible-ai-hub

  A "Responsible AI For Developers" hub to help developer audiences (students, entrepreneurs and professionals) discover workshop, events and resources that can help them learn and use Responsible AI concepts and resources effectively in their own projects.

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• GRACENGARI/customer-segmentation

  Language:Jupyter Notebook1

• Mavekepeter/ChumsApp

  CHUrch Management Software - The main web application (app.chums.org)

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