mikesmayer's Stars
Significant-Gravitas/AutoGPT
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
lobehub/lobe-chat
🤯 Lobe Chat - an open-source, modern-design AI chat framework. Supports Multi AI Providers( OpenAI / Claude 3 / Gemini / Ollama / Qwen / DeepSeek), Knowledge Base (file upload / knowledge management / RAG ), Multi-Modals (Vision/TTS/Plugins/Artifacts). One-click FREE deployment of your private ChatGPT/ Claude application.
brillout/awesome-react-components
Curated List of React Components & Libraries.
mckaywrigley/chatbot-ui
AI chat for any model.
khoj-ai/khoj
Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free.
leon-ai/leon
🧠 Leon is your open-source personal assistant.
FaridSafi/react-native-gifted-chat
💬 The most complete chat UI for React Native
PatrickJS/PatrickJS-starter
MFE Starter
mshumer/gpt-prompt-engineer
vincelwt/chatgpt-mac
ChatGPT for Mac, living in your menubar.
boxyhq/saas-starter-kit
🔥 Enterprise SaaS Starter Kit - Kickstart your enterprise app development with the Next.js SaaS boilerplate 🚀
e2b-dev/fragments
Open-source Next.js template for building apps that are fully generated by AI. By E2B.
grafana/oncall
Developer-friendly incident response with brilliant Slack integration
mcnamee/react-native-starter-kit
:rocket: A React Native boilerplate app to get you up and running very, very quickly :rocket:
ishan0102/vimGPT
Browse the web with GPT-4V and Vimium
NathanWalker/angular-seed-advanced
Advanced Angular seed project with support for ngrx/store, ngrx/effects, ngx-translate, angulartics2, lodash, NativeScript (*native* mobile), Electron (Mac, Windows and Linux desktop) and more.
DavideViolante/Angular-Full-Stack
Angular Full Stack project built using Angular, Express, Mongoose and Node. Whole stack in TypeScript.
a16z-infra/llm-app-stack
dancancro/great-big-example-application
A full-stack example app built with JHipster, Spring Boot, Kotlin, Angular 4, ngrx, and Webpack
born2net/Angular-kitchen-sink
:rocket: Angular - learn by example
chrisvxd/story2sketch
Convert Storybook into Sketch symbols 💎
zckly/create-t3-turbo-ai
Build full-stack, type-safe, LLM-powered apps with the T3 Stack, Turborepo, OpenAI, and Langchain
MorpheusAIs/Morpheus
Morpheus - A Network For Powering Smart Agents - Compute + Code + Capital + Community
enrolla/enrolla
The open source customer feature framework for B2Bs. Easily control how your product behaves and looks for different customers.
Nemshan/predicting-Paid-amount-for-Claims-Data
Introduction The context is the 2016 public use NH medical claims files obtained from NH CHIS (Comprehensive Health Care Information System). The dataset contains Commercial Insurance claims, and a small fraction of Medicaid and Medicare payments for dually eligible people. The primary purpose of this assignment is to test machine learning (ML) skills in a real case analysis setting. You are expected to clean and process data and then apply various ML techniques like Linear and no linear models like regularized regression, MARS, and Partitioning methods. You are expected to use at least two of R, Python and JMP software. Data details: Medical claims file for 2016 contains ~17 millions rows and ~60 columns of data, containing ~6.5 million individual medical claims. These claims are all commercial claims that were filed by healthcare providers in 2016 in the state of NH. These claims were ~88% for residents of NH and the remaining for out of state visitors who sought care in NH. Each claim consists of one or more line items, each indicating a procedure done during the doctor’s visit. Two columns indicating Billed amount and the Paid amount for the care provided, are of primary interest. The main objective is to predict “Paid amount per procedure” by mapping a plethora of features available in the dataset. It is also an expectation that you would create new features using the existing ones or external data sources. Objectives: Step 1: Take a random sample of 1 million unique claims, such that all line items related to each claim are included in the sample. This will result in a little less than 3 million rows of data. Step 2: Clean up the data, understand the distributions, and create new features if necessary. Step 3: Run predictive models using validation method of your choice. Step 4: Write a descriptive report (less than 10 pages) describing the process and your findings.
sushant12/jvn
A open source Nepali crowdfunding platform. Crowdfunding in Nepal
segmentio/analytics-test-apps
Test apps for iOS library.
nhutcorp/angular-template-adm
Basic admin template for Angular 4 using Material Design
sushant12/catarse.js
Mithril components for Grasruts. A open source Nepali crowdfunding platform. Crowdfunding in Nepal
TutorialsOrg/angular-travis-ci-heroku
This repo contains code as a demo to implement continuous integration with travis CI and extends it to CD with heroku