My RTX 3090 runs Llama 3.1 70B locally, but 'local' doesn't automatically mean 'private.' After discovering unexpected network traffic from Ollama, I rebuilt my AI lab with real privacy controls.

Progressive skill loading achieves 98% token reduction in LLM workflows through modular context architecture—lessons from building production systems

Complete guide to running LLMs locally for privacy: hardware requirements, model selection, optimization techniques, and practical deployment strategies

Lessons from running LLMs and AI experiments at home while keeping data secure, covering model isolation, network segmentation, and privacy controls

Effective prompt engineering techniques for LLMs—few-shot learning, chain-of-thought, system prompts, and strategies for reliable outputs

Ethical implications of LLMs—bias, misinformation, privacy, and accountability. Exploring responsible AI development and deployment frameworks

The moment I realized how LLMs confidently hallucinate facts was when I understood why RAG isn't optional - it's essential for trustworthy AI systems

Reading 'Attention is All You Need' felt like discovering a secret that would reshape everything I thought I knew about natural language processing - and it did

Running both Llama and GPT-4 in my homelab taught me the real trade-offs between open-source and proprietary LLMs beyond hype and marketing