mohofo7

mohofo7's Stars

• midday-ai/v1

  An open-source starter kit based on Midday.

  Language:TypeScript2.9k243

• Cinnamon/kotaemon

  An open-source RAG-based tool for chatting with your documents.

  Language:Python12.7k949

• hacksider/Deep-Live-Cam

  real time face swap and one-click video deepfake with only a single image

  Language:Python37.3k5.3k

• formkit/auto-animate

  A zero-config, drop-in animation utility that adds smooth transitions to your web app. You can use it with React, Vue, or any other JavaScript application.

  Language:TypeScript12.6k224

• yandricr/gpti-js

  This package simplifies your interaction with various GPT models, removing the need for tokens or other methods to access GPT

  Language:JavaScript527

• Romr1ch/react-pin-input-hook

  React hook for entering a PIN code

  Language:TypeScript173

• wldyslw/react-bottom-sheet

  React component for iOS- and Android-like bottom sheet

  Language:TypeScript91

• SasanFarrokh/vite-plugin-multi-device

  Vitejs plugin for building multiple times for multiple devices

  Language:TypeScript492

• aribudin/tailmater

  Tailmater is an open source Google Material 3 UI Kit based on Tailwind css framework.

  Language:HTML16321

• callstack/linaria

  Zero-runtime CSS in JS library

  Language:TypeScript11.6k416

• whatwg6/pos-above-keyboard

  Language:HTML53

• vaibhavjagdale/WebdriverIO-Typescript-Cucumber-BrowserStack-BoilerplateProject

  Create a powerful boilerplate for UI Automation test with WebdriverIO and Cucumber+TypeScript+BrowserStack

  Language:TypeScript51

• orval-labs/orval

  orval is able to generate client with appropriate type-signatures (TypeScript) from any valid OpenAPI v3 or Swagger v2 specification, either in yaml or json formats. 🍺

  Language:TypeScript2.9k321

• bladechapman/raft-webrtc

  An exploration in using Raft and WebRTC for distributed, consensus-driven applications

  Language:TypeScript31

• shosti/wraft

  Raft distributed consensus for WebAssembly in Rust

  Language:Rust675

• ds300/patch-package

  Fix broken node modules instantly 🏃🏽‍♀️💨

  Language:TypeScript10.4k289

• beekai-oss/little-state-machine

  📠 React custom hook for persist state management

  Language:TypeScript1.5k53

• alireza-mh/SEO-Checklist

  Provide checklist and tools to help with SEO of your website/app

  8

• SassNinja/media-query-plugin

  Webpack plugin for media query extraction.

  Language:JavaScript20527

• cyrilwanner/next-optimized-images

  🌅 next-optimized-images automatically optimizes images used in next.js projects (jpeg, png, svg, webp and gif).

  Language:JavaScript2.2k93

• hc-oss/react-multi-select-component

  Lightweight (~5KB gzipped) multiple selection dropdown component

  Language:TypeScript38689

• ffmpegwasm/ffmpeg.wasm

  FFmpeg for browser, powered by WebAssembly

  Language:C14.2k830

• NourozR/Stock-Price-Prediction-LSTM

  OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network

  Language:Python564213

• GR34SE/react-typescript-starter

  Minimalist React 18 starter template with TypeScript ⚛

  Language:TypeScript9315

• vikpe/react-webpack-typescript-starter

  (DEPRECATED) Minimal starter with hot module replacement (HMR) for rapid development.

  Language:JavaScript855223

• alevchuk/pairwise-alignment-in-python

  Pairwise string alignment in Python (Needleman-Wunsch and Smith-Waterman algorithms)

  Language:Python9650

• duemig/Stanford-Project-Predicting-stock-prices-using-a-LSTM-Network

  Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

  Language:Jupyter Notebook24452

• xiaofuzi/rollup-plugin-less

  a less rollup plugin

  Language:JavaScript2223

• aminabedi68/Estedad

  Sans Serif Arabic-Latin text typeface

  Language:Python27613

• alireza-mh/iyarn

  yarn for Iran

  Language:TypeScript7