This tutorial demonstrates how to integrate Google’s Gemini 2.0 with an in-process Model Context Protocol (MCP) server using FastMCP, creating tools for weather information and integrating them into Gemini's function calling workflow.
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.
This tutorial details how to use FastAPI-MCP to convert a FastAPI endpoint (fetching US National Park alerts) into an MCP-compatible server. It covers environment setup, app creation, testing, and MCP server implementation with Cursor IDE.
This example demonstrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN) using scikit-learn, showing how to generate synthetic clusters, compute DBSCAN clustering, and visualize the results, including core and non-core samples.
This document details how to run Qwen models locally using the Text Generation Web UI (oobabooga), covering installation, setup, and launching the web interface.
An overview of the M5Stack Cardputer, a compact, card-sized portable computer with an ESP32-S3 chip, keyboard, screen, and various peripherals. It's aimed at engineers, developers, and IoT enthusiasts, with support for Python, Arduino, and even a Forth interpreter.
Model Context Protocol server to run Python code in a sandbox using Pyodide in Deno, isolated from the operating system.
This article details a method for training large language models (LLMs) for code generation using a secure, local WebAssembly-based code interpreter and reinforcement learning with Group Relative Policy Optimization (GRPO). It covers the setup, training process, evaluation, and potential next steps.
A popular and actively maintained open-source web crawling library for LLMs and data extraction, offering advanced features like structured data extraction, browser control, and markdown generation.
This article provides practical tips for improving the quality and effectiveness of unit tests in Python, covering aspects like test organization, mocking, test-driven development, and avoiding common pitfalls.
