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Generative AI with Local LLMs

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Local LLM

A collection of 7 posts

Modern AI Integrations: MCP Server Meets REST API and Local LLMs — Part 3 (MCP Agent Integration)

Modern AI Integrations: MCP Server Meets REST API and Local LLMs — Part 3 (MCP Agent Integration)

In the final part of our series, we demonstrate how to build a complete real-world workflow using the MCP Agent to interact with an MCP Server that exposes a RESTful inventory service. If you’ve followed along with Part 1 and Part 2, you now have an MCP-compliant FastAPI app

Modern AI Integrations: MCP Server Meets REST API and Local LLMs, Part 2

Modern AI Integrations: MCP Server Meets REST API and Local LLMs, Part 2

In Part 1, we built a FastAPI-based application to manage warehouse orders and exposed it as an MCP server. We demonstrated how the Model Context Protocol (MCP) can be used to describe APIs in a structured way and make them discoverable to AI agents or MCP client. Now, in Part

Modern AI Integrations: MCP Server Meets REST API and Local LLMs, Part 1

Modern AI Integrations: MCP Server Meets REST API and Local LLMs, Part 1

As AI workflows become more modular and service-driven, integrating local LLMs into existing infrastructure becomes essential. One way to achieve this is through the Model Context Protocol (MCP), which allows services and models to expose structured, discoverable metadata. In this post, we demonstrate how to connect an MCP server with

Turn Ideas into Income: Create AI-Driven Content Pipelines with Local LLMs

Turn Ideas into Income: Create AI-Driven Content Pipelines with Local LLMs

The rise of Large Language Models (LLMs) has completely changed how we create and distribute content. From blog posts to documentation, tutorials to newsletters, what used to take hours can now be done in minutes. But even better—you can now turn this content into income with ease. And you

Model Context Protocol (MCP): Connecting Local LLMs to Various Data Sources

Model Context Protocol (MCP): Connecting Local LLMs to Various Data Sources

Anthropic launched the Model Context Protocol (MCP) in November 2024 — an open standard for data exchange between LLMs and various data sources. The protocol provides a simplified way for LLMs to integrate with tools and services to perform tasks such as searching files on local systems, accessing GitHub repositories to

Minion(s): A Simple Protocol for Communicating with Local and Cloud LLMs

Minion(s): A Simple Protocol for Communicating with Local and Cloud LLMs

Recently, HazyResearch introduced a simple communication protocol for integrating local and cloud LLMs. The core idea behind the protocol is to maximize the use of local LLM models with local data, minimizing cloud API costs while maintaining high-quality outputs. The protocol comes in two flavors: * Minion: A local LLM model

First step to the world of AI: installing a local LLM

First step to the world of AI: installing a local LLM

Installing a local LLM After a long time, I've decided to embark on my journey with AI. Over this period, significant progress has been made in the field. Today, we're surrounded by numerous Large Language Model (LLM) models, tools, and agents. It's therefore timely