AI Tutorials

Hands-on AI tutorials covering deep learning, NLP, computer vision, LLM apps, RAG pipelines, and AI agents. Step-by-step guides for every skill level.

• Claude Code Projects Tutorial: Run Parallel Cloud Threads With Shared Memory (Beta) Step-by-step guide to Claude Code Projects (public beta, September 2026): create a project, write standing instructions, send a batch of work to parallel cloud threads, review pull requests, and control cost. AI Agents · Intermediate · 12 min read
• Hybrid Search: BM25 + Vector Similarity (With Code) Why relying solely on vector search for RAG is a mistake. Learn how to implement Hybrid Search combining lexical (BM25) and semantic (Vector) search using LangChain and multiple retrievers. LLM Applications · Advanced · 18 min read
• Fine-Tuning Llama 3 on Custom Data (Step-by-Step) A complete walkthrough of fine-tuning Llama 3 8B using QLoRA and Unsloth. Learn how to prepare your dataset, configure hyperparameters, and train on a single GPU. LLM Engineering · Advanced · 25 min read
• CrewAI Tutorial: Build a Multi-Agent Research Team Learn how to orchestrate autonomous AI agents using CrewAI. We'll build a team of specialized agents (Researcher, Writer, Editor) to automate content creation. AI Agents · Intermediate · 18 min read
• vLLM Quick-Start: Serve Any LLM in 5 Minutes Learn how to deploy an open-source LLM for high-throughput production serving using vLLM and PagedAttention, complete with an OpenAI-compatible API. LLM Engineering · Advanced · 12 min read
• Local RAG Stack: Ollama + ChromaDB + LangChain Build a completely private, zero-cost Retrieval-Augmented Generation pipeline using Ollama for local LLMs, ChromaDB for vector storage, and LangChain. LLM Applications · Intermediate · 20 min read
• LangChain RAG Tutorial in Python A practical LangChain RAG tutorial in Python covering document loading, chunking, embeddings, vector storage, retrieval, and answer generation. LLM Applications · Intermediate · 22 min read
• How to Run an LLM Locally on Mac with Ollama Learn how to run LLMs locally on a Mac using Ollama. Covers Apple Silicon requirements, model setup, commands, performance tips, and common issues. LLM Applications · Beginner · 18 min read
• Prompt Engineering Patterns That Actually Work (2026 Edition) Stop guessing with your prompts. Learn the proven, reliable prompt engineering patterns that consistently unearth the best performance from modern LLMs. LLM Applications · Beginner · 15 min read
• How to Build an AI Agent with LangGraph and Claude Learn how to build a robust, multi-step AI agent using LangGraph and the Claude API, complete with tool use and human-in-the-loop approvals. AI Agents · Intermediate · 25 min read
• Building Custom OpenClaw Skills: Extend Your AI Agent Create custom AgentSkills for OpenClaw: define permissions, schemas, functions, test locally, and publish safely. AI Agents · Intermediate · 25 min read
• Getting Started with OpenClaw: Your Personal AI Assistant Install OpenClaw locally, connect messaging channels and skills, and harden security — your private, extensible AI agent. AI Agents · Beginner · 18 min read
• Building AI Agents with LangChain and Tool Use Build tool‑using AI agents with LangChain (ReAct/Plan‑and‑Execute), memory, tool safety, and error handling — from idea to working demo. LLM Applications · Advanced · 30 min read
• Computer Vision with Transformers: Beyond CNNs Vision Transformers (ViT) split images into patches and use attention for global context. Build, train, and compare vs CNNs. Computer Vision · Intermediate · 22 min read
• Introduction to Retrieval-Augmented Generation (RAG) RAG combines retrieval + generation to ground LLM answers. Learn the pipeline, embeddings, hybrid search, and evaluation. LLM Applications · Intermediate · 20 min read
• Fine-Tuning Large Language Models: A Practical Guide Choose full fine‑tuning vs LoRA/QLoRA, prepare clean data, configure training, evaluate rigorously, and ship fast inference — a practical playbook. NLP · Advanced · 25 min read
• Building Your First Neural Network with PyTorch Build and train your first PyTorch neural network end‑to‑end: tensors, nn.Module, training loops, metrics — perfect for beginners. Deep Learning · Beginner · 15 min read