Leveraging MCP for automating your daily routine. This article explores the Model Context Protocol (MCP) and demonstrates how to build a toolkit for analysts using it, including creating a local MCP server with useful tools and integrating it with AI tools like Claude Desktop.
This practical guide uses SERP comparisons and Python to group keywords by intent, faster and more intuitively.
Optuna is an open-source hyperparameter optimization framework designed to automate the hyperparameter search process for machine learning models. It supports various frameworks like TensorFlow, Keras, Scikit-Learn, XGBoost, and LightGBM, offering features like eager search spaces, state-of-the-art algorithms, and easy parallelization.
Browser Use is a library that enables AI agents to interact with web browsers, making websites accessible for automated tasks. It includes features for browser automation, agent memory, and various demos showcasing its capabilities.
This article explores ten underrated Python libraries that can help automate tasks, debug faster, and improve coding efficiency.
- **Rich**: Terminal beautification
- **PyWhatKit**: Automation tasks
- **Pydantic**: Data validation
- **Black**: Code formatting
- **HTTPie**: API testing
- **Typer**: Building CLI applications
- **IceCream**: Debugging
- **Poetry**: Package management
- **Faker**: Generating fake data
- **Pyppeteer**: Browser automation
- **Rich**: Terminal beautification
- **PyWhatKit**: Automation tasks
- **Pydantic**: Data validation
- **Black**: Code formatting
- **HTTPie**: API testing
- **Typer**: Building CLI applications
- **IceCream**: Debugging
- **Poetry**: Package management
- **Faker**: Generating fake data
- **Pyppeteer**: Browser automation
A step-by-step guide on automating the execution of Jupyter Notebooks and generating HTML reports using Python scripts. The article explains how Jupyter Notebooks can be used for creating interactive reports and how their execution can be synchronized with data pipelines to update reports automatically.
This article explores automating the process of converting scientific code into LaTeX documents using GPT models and Python, aiming to streamline documentation workflows in scientific projects.
