Step-by-step practical guides for running local AI, building agents, writing prompts, calling APIs, fine-tuning models, and keeping your data private.
Ollama is the fastest way to run open-source LLMs locally. This guide walks you through installation on macOS, Linux, and Windows, pulling your first model, and running it from the terminal or via its REST API.
Read guideLM Studio is a desktop app for running local AI models without any terminal commands. This guide covers installation, downloading a model from Hugging Face, and starting a local inference server.
Read guideDeepSeek R1 is one of the most capable open-source reasoning models available. This guide covers running the 7B, 14B, and 70B distilled variants on consumer hardware using Ollama or LM Studio.
Read guideLlama 3.3 70B is Meta's best dense open-source model, matching GPT-4o on many benchmarks. This guide covers hardware requirements, quantisation selection, and optimal settings for running it on a high-end workstation or Apple Silicon Mac.
Read guideQuantisation lets you run models that would not otherwise fit in your GPU's VRAM. This guide explains the different GGUF quantisation levels, the quality-size tradeoff at each level, and which to choose for your specific hardware.
Read guideClaude Code is Anthropic's agentic coding tool that runs in your terminal. It reads your entire codebase, writes and edits files, runs tests, and executes shell commands. This guide covers installation, configuration, and practical workflows.
Read guideCursor is a VS Code fork with deep AI integration. This guide covers installing Cursor, importing your existing VS Code settings, configuring AI models, and using Cursor's Composer for multi-file editing.
Read guideGitHub Copilot has evolved far beyond autocomplete. This guide covers the completion model, Copilot Chat, Copilot CLI, and the techniques that get the most out of each feature.
Read guideCodestral is Mistral AI's code-specialised model with support for 80+ programming languages. This guide covers running it locally via Ollama for fast, offline coding assistance.
Read guideHermes is NousResearch's open-source model family trained specifically for function calling and agentic workflows. This guide covers running Hermes locally, defining tools in JSON Schema, and building a basic tool-calling loop.
Read guideReAct (Reason + Act) is the foundational pattern for LLM agents. This guide builds a working ReAct agent from scratch using the OpenAI API and function calling, with a web search and calculator tool.
Read guideLangGraph is LangChain's framework for building stateful, multi-agent workflows as directed graphs. This guide covers graph basics, state management, and building a practical multi-agent pipeline.
Read guiden8n is an open-source workflow automation tool with native LLM support. This guide covers self-hosting n8n, connecting it to OpenAI or a local Ollama model, and building an AI-powered automation workflow.
Read guideThe system prompt is the most powerful lever you have over LLM behaviour. This guide covers the key components of an effective system prompt, common mistakes, and battle-tested patterns for production use.
Read guideChain-of-thought (CoT) prompting dramatically improves LLM performance on multi-step reasoning tasks. This guide explains when and how to use it, from simple 'think step by step' to structured CoT templates.
Read guideFew-shot prompting uses examples to teach LLMs new formats and behaviours without fine-tuning. This guide explains how to construct effective examples and when few-shot beats zero-shot.
Read guideA prompt library treats prompts like code — versioned, tested, and reusable. This guide covers structuring a prompt library, versioning strategies, and tooling for testing prompt quality.
Read guideA practical introduction to the OpenAI API covering authentication, the chat completions endpoint, streaming, error handling, and cost management — with working code in Python and JavaScript.
Read guideThe Anthropic API gives access to the Claude model family. This guide covers authentication, the Messages API, vision inputs, tool use, and the key differences from the OpenAI API format.
Read guideOpenRouter is a unified API that gives access to 200+ LLM models through a single endpoint. This guide covers setup, model selection, cost optimisation, and fallback routing.
Read guideStreaming makes LLM applications feel instant by displaying tokens as they are generated. This guide covers the Server-Sent Events protocol, implementing streaming in Express + React, and handling edge cases.
Read guideUnsloth is an optimised fine-tuning library that makes LoRA training 2–5× faster with 70% less VRAM. This guide walks through fine-tuning a Llama or Mistral model on your own data using Unsloth on a free Google Colab GPU.
Read guideThe quality of your training data is the biggest factor in fine-tuning success. This guide covers data collection strategies, formatting standards, quality filtering, and the minimum viable dataset size for different tasks.
Read guideKnowing when your fine-tuned model is actually better than the base model requires systematic evaluation. This guide covers benchmark datasets, LLM-as-judge evaluation, and metrics for task-specific assessment.
Read guideFor healthcare, legal, financial, and personal data, running AI completely offline ensures your data never leaves your machine. This guide covers the full offline setup stack and practical workflows.
Read guidePrompt injection attacks trick LLMs into ignoring their system prompt or executing unintended instructions. This guide covers the attack patterns, defence strategies, and code-level mitigations for production LLM applications.
Read guidellama.cpp includes a built-in HTTP server that exposes an OpenAI-compatible API. This guide covers compiling llama.cpp with GPU support, starting the server, and deploying it for team use.
Read guideRetrieval-Augmented Generation (RAG) extends LLMs with your own documents, giving accurate, source-grounded answers without fine-tuning. This guide builds a working RAG system from document ingestion to response generation.
Read guideCrewAI lets you define teams of specialised AI agents that collaborate to complete complex tasks. This guide covers creating agents with roles, assigning tasks, and running a complete crew pipeline.
Read guideMicrosoft AutoGen enables multi-agent conversations where AI agents and human proxies collaborate to solve tasks. This guide covers setting up AutoGen, creating conversable agents, and running code-generation workflows.
Read guideThe OpenAI Assistants API provides built-in thread management, file search, and code interpreter — removing the plumbing work from agentic applications. This guide covers creating an assistant, managing threads, and using built-in tools.
Read guideContinue is an open-source VS Code and JetBrains extension that adds AI code completions and chat using local or cloud models. This guide covers installation, configuring Ollama as the backend, and effective usage patterns.
Read guideAider is an open-source terminal tool that uses LLMs to edit your code directly. Unlike IDE plugins, Aider understands your full git history and makes commits automatically. This guide covers setup, the file context system, and effective workflows.
Read guideGitHub Copilot Workspace lets you describe a task in natural language and get a fully planned, multi-file implementation. This guide covers how Workspace reasons about your repository and how to guide it effectively.
Read guidevLLM is the leading open-source LLM inference server, offering 24× higher throughput than HuggingFace Transformers through PagedAttention. This guide covers installation, serving a model, and configuring for production workloads.
Read guideText Generation Web UI is a browser-based interface for running local LLMs. It supports GGUF, GPTQ, and Transformers models, includes a chat UI and API server, and runs on any hardware. This guide covers installation and key features.
Read guideGoogle Gemini 2.5 Pro and Flash offer long context windows, multimodal inputs, and competitive pricing. This guide covers getting started with the Gemini API, function calling, and using the 1M token context window effectively.
Read guideMistral AI offers high-performance European models including Mistral Large, Pixtral for vision, and Codestral for code. This guide covers the API, function calling, and choosing the right Mistral model for your task.
Read guideRate limit errors are the most common production issue in LLM applications. This guide covers exponential backoff, token budget management, request queuing, and provider-specific rate limit strategies.
Read guidePrompt caching lets providers reuse computed KV cache from previous requests when the same prefix is repeated, dramatically reducing cost and latency. This guide covers Anthropic and OpenAI caching, and how to structure prompts for maximum cache hits.
Read guideDPO trains models to prefer good responses over bad ones using human preference data — without the complexity of reinforcement learning. This guide covers collecting preference data, training with TRL's DPO trainer, and evaluating results.
Read guideAxolotl is a config-driven fine-tuning framework that wraps HuggingFace and Unsloth with a single YAML configuration. This guide covers setting up Axolotl, writing a config, and running multi-GPU training.
Read guideGetting LLMs to output reliable JSON, YAML, or custom formats is essential for production applications. This guide covers JSON mode, JSON schema enforcement, grammar-based sampling, and fallback strategies.
Assigning a specific role or persona to an LLM consistently improves output quality on specialist tasks. This guide explains why role prompting works, how to craft effective personas, and when it helps most.
Read guideGPT-4o can analyse images, documents, charts, and screenshots in detail. This guide covers sending images via URL and base64, practical use cases, and building an image analysis pipeline.
Read guideWhisper is OpenAI's speech recognition model that supports 99 languages with near-human accuracy. This guide covers the Whisper API, running Whisper locally for privacy, and building a transcription pipeline for long recordings.
Read guideOpenAI's image generation models produce high-quality images from text descriptions. This guide covers the Images API, prompt writing for consistent results, using the edit endpoint, and integrating image generation into applications.
Read guideTraditional RAG ignores charts, diagrams, and images embedded in documents. Multimodal RAG retrieves and reasons over both text and visual content. This guide covers document parsing, image embedding, and hybrid retrieval.
Read guideText embeddings convert text into dense vector representations that capture semantic meaning. This guide covers generating embeddings with OpenAI and local models, measuring similarity, and practical applications.
Semantic search finds relevant results based on meaning rather than keywords. This guide builds a complete semantic search system from document ingestion to ranked retrieval, using embeddings and a vector store.
Read guideVector databases are purpose-built for storing and querying embeddings at scale. This guide compares the leading options — Pinecone, Weaviate, Qdrant, pgvector, and Chroma — and helps you choose the right one for your use case.
Embedding model choice significantly affects search quality and cost. This guide compares the leading models — OpenAI text-embedding-3, Cohere embed, BAAI/bge, and others — across quality, speed, cost, and language support.
Read guideLangSmith is LangChain's observability platform for logging, tracing, and evaluating LLM applications. This guide covers setup, automatic tracing, custom traces, and using the dashboard to debug production issues.
Read guidePromptfoo is an open-source CLI for testing, evaluating, and comparing LLM prompts. This guide covers writing test cases in YAML, running evaluations, comparing models, and catching prompt regressions in CI.
Read guideLLM API costs can grow unexpectedly as usage scales. This guide covers cost attribution, per-request tracking, anomaly detection, and practical techniques that reduce costs by 50–80% without sacrificing quality.
Read guideOpenTelemetry (OTel) is the vendor-neutral standard for distributed tracing. The GenAI semantic conventions extend it to LLM calls. This guide covers setting up OTel tracing for LLM applications, exporting to Jaeger or Grafana, and the GenAI conventions.
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