60 courses · 1180 chapters. A free, operator-curated path from transformer foundations through building RAG and agents to running models in production. Every chapter is plain markdown — no signup, no video wall.
Start here. How models think, what the numbers mean, why local inference behaves the way it does.
Wire things together — RAG, agents, tools, fine-tuning, evaluation. Theory turns into a working stack.
Run it for real — serving, scaling, monitoring, cost, security. The work that starts after the demo.
Start here. How models think, what the numbers mean, why local inference behaves the way it does.
Learn what is local ai — and why it matters through RunLocalAI's practical lens: ai, local, introduction and concepts, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn python for ai — zero to useful through RunLocalAI's practical lens: python, programming, data and api, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn ollama — installation to mastery through RunLocalAI's practical lens: ollama, setup, models and cli, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Read a model card and know exactly what it means. Choose the right model for any task.
Learn prompt engineering fundamentals through RunLocalAI's practical lens: prompts, engineering and llm, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn hardware planning for local ai through RunLocalAI's practical lens: hardware, gpu, vram and budget, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn local ai on windows through RunLocalAI's practical lens: windows, wsl2, cuda and docker, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn local ai on macos through RunLocalAI's practical lens: macos, apple silicon, metal and mlx, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn local ai on linux through RunLocalAI's practical lens: linux, cuda, server and docker, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn first local chatbot through RunLocalAI's practical lens: chatbot, fastapi, html and javascript, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn langchain for local ai through RunLocalAI's practical lens: langchain, chains, rag and prompts, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn vector stores and embeddings through RunLocalAI's practical lens: embeddings, vector, chromadb and faiss, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn rag systems: part 1 through RunLocalAI's practical lens: rag, retrieval, chunking and ingestion, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn rag systems: part 2 through RunLocalAI's practical lens: rag, reranking, query and optimization, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn content creation with local ai through RunLocalAI's practical lens: content, writing, editing and blogging, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn capstone: first ai product through RunLocalAI's practical lens: capstone, product, project and full stack, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Wire things together — RAG, agents, tools, fine-tuning, evaluation. Theory turns into a working stack.
Learn introduction to ai agents through RunLocalAI's practical lens: agents, tools, function calling and react, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn langgraph for local agents through RunLocalAI's practical lens: langgraph, state, multi agent and workflows, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn advanced rag — chunking, retrieval, re-ranking through RunLocalAI's practical lens: rag, chunking, hybrid search and reranking, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn multi-agent systems through RunLocalAI's practical lens: agents, multi agent, orchestration and supervisor, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn fine-tuning with lora and qlora through RunLocalAI's practical lens: finetuning, lora, qlora and training, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn production local ai deployment through RunLocalAI's practical lens: deployment, docker, kubernetes and monitoring, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn voice ai with local models through RunLocalAI's practical lens: voice, stt, tts and whisper, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn mcp server implementation through RunLocalAI's practical lens: mcp, model context protocol, servers and tools, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn local ai for code generation through RunLocalAI's practical lens: code generation, continue dev, fim and autocomplete, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn document processing with local ai through RunLocalAI's practical lens: documents, pdf, ocr and classification, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn local ai apis and integration through RunLocalAI's practical lens: api, fastapi, openai compatible and streaming, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn multi-modal ai: vision and text through RunLocalAI's practical lens: vision, multimodal, llava and image, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn business automation with local ai through RunLocalAI's practical lens: automation, business, email and reports, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn advanced prompt engineering through RunLocalAI's practical lens: prompts, chain of thought, dspy and optimization, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn rag evaluation and metrics through RunLocalAI's practical lens: evaluation, ragas, metrics and retrieval, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn data analysis with local ai through RunLocalAI's practical lens: data analysis, pandas, visualization and nlp, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn function calling for local models through RunLocalAI's practical lens: function calling, tools, json schema and ollama, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn model optimization for local inference through RunLocalAI's practical lens: optimization, quantization, pruning and speculative decoding, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn custom training pipelines through RunLocalAI's practical lens: training, pipelines, distributed and huggingface, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn healthcare ai with local models through RunLocalAI's practical lens: healthcare, hipaa, clinical and privacy, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn edge ai: mobile and iot through RunLocalAI's practical lens: edge, mobile, iot and raspberry pi, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn capstone: full-stack ai app through RunLocalAI's practical lens: capstone, fullstack, app and deployment, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Run it for real — serving, scaling, monitoring, cost, security. The work that starts after the demo.
Learn enterprise-scale rag through RunLocalAI's practical lens: enterprise, rag, distributed and kafka, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn custom llm architecture design through RunLocalAI's practical lens: architecture, transformers, attention and pytorch, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn rlhf, dpo, and ppo through RunLocalAI's practical lens: rlhf, dpo, ppo and alignment, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn local ai clusters through RunLocalAI's practical lens: clusters, distributed, parallelism and kubernetes, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn advanced multi-modal systems through RunLocalAI's practical lens: video, multimodal, temporal and audio visual, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn custom agent frameworks through RunLocalAI's practical lens: agents, framework, runtime and memory, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn mlops for local ai through RunLocalAI's practical lens: mlops, mlflow, ci cd and drift, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn deepseek r1 and reasoning models through RunLocalAI's practical lens: reasoning, deepseek, r1 and chain of thought, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn ai products with local models through RunLocalAI's practical lens: product, strategy, monetization and naira, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn vector database internals through RunLocalAI's practical lens: vectordb, hnsw, ivf and quantization, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn ai safety and alignment through RunLocalAI's practical lens: safety, alignment, red teaming and interpretability, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn custom quantization and kernels through RunLocalAI's practical lens: quantization, cuda, kernels and tensorrt, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn local ai for scientific research through RunLocalAI's practical lens: research, science, literature and hypothesis, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn hybrid local-cloud ai architecture through RunLocalAI's practical lens: hybrid, cloud, routing and cost optimization, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn advanced nlp with local models through RunLocalAI's practical lens: nlp, ner, classification and summarization, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn ai-powered saas products through RunLocalAI's practical lens: saas, multi tenant, billing and paystack, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn model compression through RunLocalAI's practical lens: compression, pruning, distillation and pipeline, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn openclaw: building a personal ai agent through RunLocalAI's practical lens: personal agent, memory, tools and scheduling, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Deploy offline-first AI solutions for African users with local language support, covering Yoruba, Hausa, and Igbo language models, low-resource optimization, and agricultural AI applications.
Learn capstone: research ai system through RunLocalAI's practical lens: capstone, research, paper and ablation, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn security and privacy for local ai through RunLocalAI's practical lens: security, privacy, api and auth, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
Learn troubleshooting local ai through RunLocalAI's practical lens: troubleshooting, debugging, errors and diagnosis, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.