Build privacy-first AI lab with local LLMs—run models up to 34B on RTX 3090 (24GB VRAM) with network isolation, traffic monitoring, and real privacy controls.

Optimize LLM workflows with progressive context loading—achieve 98% token reduction using modular architecture for efficient production deployments.

Deploy local LLMs for privacy-first AI—run language models on homelab hardware with model selection, optimization, and deployment strategies.

Secure personal AI experiments with model isolation and network segmentation—protect LLM deployments using privacy controls and threat modeling.

Automate security alert analysis using local LLMs (Ollama) for privacy-preserving incident response. Reduce alert fatigue with AI-powered triage without cloud dependencies.

Run LLaMA 3.1 on Raspberry Pi with PIPELOAD pipeline inference—achieve 90% memory reduction and deploy 7B models on 8GB edge devices at 2.5 tokens/sec.

Train embodied AI agents with vision, language, and physical interaction—build robots that learn from real environments using reinforcement learning.

Master prompt engineering with few-shot learning and chain-of-thought techniques—improve LLM response quality by 40% through systematic optimization.

Address LLM ethics including bias, privacy, and accountability—implement responsible AI frameworks for large language model deployment in production.

Build RAG systems with vector databases and semantic search—eliminate LLM hallucinations and ground responses in verified knowledge for trustworthy AI.

Master transformer architecture with self-attention and positional encoding—understand the foundation of GPT-4, BERT, and modern language models.

Compare open-source vs proprietary LLMs with Llama 3 and GPT-4 benchmarks—understand performance, cost, and customization trade-offs for production.