Free guides cover local LLM setup, AI agent building, and cutting API costs by 60%

A GitHub repository by jamesob offers a detailed guide for running open-source LLMs on local hardware, covering quantization, hardware requirements, and offline deployment to reduce cloud costs. A separate project called pxpipe proposes converting code into images and using OCR before LLM processing, reportedly cutting API costs by up to 60% by exploiting cheaper image-token pricing in multimodal models like GPT-4o. A free 84-page handbook published on DEV Community walks readers from basic concepts such as tokens and embeddings all the way to building functional AI agents with tool use and planning capabilities. Together, these three resources target developers seeking practical, cost-effective frameworks for real-world AI application development. All materials are freely available online and focus on actionable, production-ready workflows.