machine-learning

How I went from naive round-robin model selection to a five-stage routing pipeline backed by RouteLLM, TOPSIS, and LinUCB research. The failures that led to each improvement.

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.

Deploy Vision-Language-Action models for embodied AI robots—integrate physical world interaction with security considerations for homelab automation.

Deploy Claude-Flow AI agent swarms for development—achieve 84.8% SWE-Bench solve rate with neural learning and multi-agent orchestration for complex tasks.

Fine-tune LLMs on homelab hardware with QLoRA and 4-bit quantization. Train Llama 3 8B models on RTX 3090 with dataset prep and optimization strategies.

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

Deploy federated learning across homelab with granular-ball computing—train privacy-preserving models with 82% reduced network transfer.

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.

Build multimodal AI systems with GPT-4 Vision and CLIP—process text, images, and audio together for next-generation foundation model applications.

Train AI models on resource-constrained hardware with quantization, pruning, and distillation—run GPT-3 capabilities 100x faster through compression.

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