A nice web serial plotter for Arduino/ESP32/Microcontroller projects. It's a real-time, beautiful, and zero-friction plotting tool for serial data, built with Vite + React + TypeScript + Tailwind CSS. It offers features like multi-series plotting, interactive controls, data analysis, channel management, export options, a serial console, and a built-in signal generator.
A no-install needed web-GUI for Ollama. It provides a web-based interface for interacting with Ollama, offering features like markdown rendering, keyboard shortcuts, a model manager, offline/PWA support, and an optional API for accessing more powerful models.
Build, enrich, and transform datasets using AI models with no code. This repository provides the source code for Hugging Face AI Sheets, an open-source tool for dataset manipulation using AI.
An open-source background coding agent. Designed to understand, reason about, and contribute to existing codebases. Licensed for open-source use under MIT License. It sets up isolated execution environments for AI agents to work on GitHub repositories with tools to understand code, edit files, and much more.
ollamark is a command-line client for Ollama with markdown support. It allows users to execute prompts with various options like model selection, system prompts, temperature control, and output formatting (JSON, HTML).
A Discord bot for Meshtastic network integration, enabling communication between Meshtastic mesh networks and Discord channels. It is the sucessor to the original bot: https://github.com/baymesh/ratm-meshtastic-discord-bot
This article details the creation of a simple, 50-line agent using Model Context Protocol (MCP) and Hugging Face's tools, demonstrating how easily agents can be built with modern LLMs that support function/tool calling.
1. **MCP Overview**: MCP is a standard API for exposing tools that can be integrated with Large Language Models (LLMs).
2. **Implementation**: The author explains how to implement a MCP client using TypeScript and the Hugging Face Inference Client. This client connects to MCP servers, retrieves tools, and integrates them into LLM inference.
3. **Tools**: Tools are defined with a name, description, and parameters, and are passed to the LLM for function calling.
4. **Agent Design**: An agent is essentially a while loop that alternates between tool calling and feeding tool results back into the LLM until a specific condition is met, such as two consecutive non-tool messages.
5. **Code Example**: The article provides a concise 50-line TypeScript implementation of an agent, demonstrating the simplicity and power of MCP.
6. **Future Directions**: The author suggests experimenting with different models and inference providers, as well as integrating local LLMs using frameworks like llama.cpp or LM Studio.
1. **MCP Overview**: MCP is a standard API for exposing tools that can be integrated with Large Language Models (LLMs).
2. **Implementation**: The author explains how to implement a MCP client using TypeScript and the Hugging Face Inference Client. This client connects to MCP servers, retrieves tools, and integrates them into LLM inference.
3. **Tools**: Tools are defined with a name, description, and parameters, and are passed to the LLM for function calling.
4. **Agent Design**: An agent is essentially a while loop that alternates between tool calling and feeding tool results back into the LLM until a specific condition is met, such as two consecutive non-tool messages.
5. **Code Example**: The article provides a concise 50-line TypeScript implementation of an agent, demonstrating the simplicity and power of MCP.
6. **Future Directions**: The author suggests experimenting with different models and inference providers, as well as integrating local LLMs using frameworks like llama.cpp or LM Studio.
Powering the future of open-source AI agent development. Discover, run, and compose AI agents from any framework. Build production-grade AI agents in both Python and Typescript. Join our community on Discord, BlueSky, and YouTube.
This document details how to use function calling with Mistral AI models to connect to external tools and build more complex applications, outlining a four-step process: User query & tool specification, Model argument generation, User function execution, and Model final answer generation.
This article explores emerging JavaScript patterns anticipated to become significant in 2025, including Pattern Matching, Decorators, Module Federation, Proxy-Based Observables, and Immutable Data Patterns. Each pattern is discussed with its current status, importance, and potential impact on future web development.
