AI Tools on Mohammad Movahedi

Local LLMs 209: A Practical Local Agent Stack

Fri, 05 Jun 2026 00:00:00 +0000

After the tools, models, routers, agents, skills, and MCP servers, it helps to put the pieces into one practical stack.

The goal is not to build the most complicated local AI system. The goal is to build a workflow you can understand, debug, and improve.

Mental model: start local, add power only when needed

A practical beginner stack has layers:

Layer	Default choice	Why
Runtime	Ollama or another beginner-friendly local server	Gets a model running quickly
Model	Small or medium instruct model	Establishes a stable baseline
API shape	OpenAI-compatible endpoint when possible	Works with many clients
Evaluation	Fixed prompt set	Prevents vibe-based model choice
Tools	MCP servers or app-native tools	Adds controlled access to context
Instructions	Rules, skills, or project notes	Makes behavior repeatable
Hosted fallback	OpenRouter or direct provider API	Handles tasks local models cannot
Coding agent	Claude Code, Codex, Antigravity, or another tool	Handles repository actions and verification

The Practical Local Agent Stack

Click a layer to see when to add it

Local LLMs 208: MCP and How Local LLMs Get Tools and Context

Thu, 04 Jun 2026 00:00:00 +0000

A local model by itself only sees the prompt you give it. It cannot read your files, inspect GitHub issues, search a database, or check a calendar unless something connects it to those sources.

MCP, the Model Context Protocol, is one way to standardize that connection.

Mental model: MCP is a tool plug

Anthropic’s MCP documentation describes MCP as an open protocol that standardizes how applications provide context to LLMs. The common analogy is a standard connector between AI applications and data or tools.

Local LLMs 202: The Local LLM Toolchain: uv, uvx, Ollama, and Model Files

Fri, 29 May 2026 00:00:00 +0000

A local LLM setup can feel messy because several tools appear at once: Python environments, command-line utilities, model downloaders, model runtimes, chat interfaces, and APIs. The trick is to separate the toolchain into jobs.

For a beginner, four ideas are enough to get oriented: uv, uvx, Ollama, and model files.

Mental model: tools install things, runtimes run models

uv helps manage Python projects, Python versions, dependencies, and Python command-line tools. Ollama runs language models and exposes them through a local API. A model file contains the model weights and metadata that the runtime needs.

Local LLMs 201: What Runs on Your Machine

Thu, 28 May 2026 00:00:00 +0000

Local large language models are exciting because they move part of the AI stack onto your own computer. Instead of sending every prompt to a remote service, you download a model, run it through a local runtime, and talk to it from an app, terminal, editor, or API.

The beginner mistake is to ask, “What is the best local model?” The better first question is: what can this machine actually run well enough to be useful?