naimkatiman

Pinned Repositories

• Ai-agency

  The only reliable agent framework built on top of the latest OpenAI Assistants API.

  Language:Python3100

• ChatGPT-Clone

  ChatGPT Clone (Free Model+Low Parameter=Slow), but hey.... I did it^^

  Language:TypeScript00

• Flutter_Mobile_app_Introduction_Animation

  Introduction Animation APP from Best Flutter UI Templates with a little bit tweaks

  Language:Dart00

• FullstackBootcamp2024

  During the boot camp, I explored web development (HTML, CSS, JavaScript, Vue, React, PHP, Laravel, Flask, Django, WordPress), backend (MySQL, MongoDB, Firebase, Redis, Node.js), DevOps (Linux, Git, Docker, Apache Kafka), cloud computing, machine learning (Python, TensorFlow, Scikit-learn), RESTful APIs, and agile methodologies.

  Language:HTML3100

• Llama-3.1-8B-Free-Version

  Set up Ollama locally with a QA component using lightrag library on Windows.

  Language:Python2200

• MLstockApp

  This repo hosts a web app that predicts stock trends using machine learning and tweet sentiment analysis. It combines historical data with real-time tweet sentiment for accurate insights. Features include trend forecasting and visualizations using Python, WordPress Flask, JS, HTML/CSS, Twitter API, Keras, Tensorflow, and scikit-learn

  Language:PHP0100

• Multi-Modal-RAG-Pipeline-on-Images-and-Text-Locally

  My first Multi-Modal RAG pipeline....Dummy version

  Language:Jupyter Notebook3100

• RAG-using-Llama-3.1-WebUi-on-Streamlit

  Run Llama on serverless with multimodal RAG, with focus on reading files (But with limited token ofc)

  Language:Jupyter Notebook2100

• Stock-Analysis-As-Agents-NVDA

  This project uses the CrewAI framework to automate stock analysis, enabling AI agents to collaborate and execute complex tasks efficiently. Example stock: Nvidia. Technologies include Python, CrewAI, Unstructured, PyOWM, Tools, Wikipedia, yFinance, SEC-API, tiktoken, faiss-cpu, python-dotenv, langchain-community, langchain-core, and OpenAI.

  Language:Python1100

• Stock-Market-Prediction-Using-PythonML

  Predicting S&P 500 closes with machine learning. Analyzes historical data (OHLCV) to train models for future price forecasts. Evaluates different models' performance.

  Language:Python0100

naimkatiman's Repositories