ai

How I used AI-assisted investigation to triage the trivy-action supply chain attack across my homelab repos — and some thoughts on weekend incident response and community notification gaps.

The engineering story behind nexus-agents, a research-backed multi-model orchestration system that coordinates Claude, Gemini, and Codex through consensus voting, adaptive routing, and graph workflows.

How multi-model consensus voting catches blind spots that single models miss. The research behind adversarial roles, Bayesian aggregation, and structured deliberation across Claude, Gemini, and Codex.

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.

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

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

Understand AI cognitive infrastructure shaping how billions think—explore societal effects of language models transforming from tools to thought systems.

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.

Build MCP standards server for Claude AI—implement Model Context Protocol for intelligent code standards and context-aware workflows.

Transform Claude CLI with standards integration—achieve 90% token reduction and automate workflows using context-aware MCP server architecture.

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

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.

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

Monitor GPU power with NVIDIA SMI and Grafana dashboards—reduce ML training electricity costs by 40% using optimization strategies for RTX 3090.

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

Understand LLM context windows from 2K to 2M tokens—optimize model performance and prevent hallucinations at 28K token boundaries.

Test LLM smart contract security with GPT-4 and Claude—achieve 80% reentrancy detection accuracy but manage 38% false positives in production workflows.

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

Deploy AI edge computing with YOLOv8 and TensorFlow Lite—achieve 15ms latency for real-time inference on Raspberry Pi with local processing for privacy.

Deploy AI-powered cybersecurity with automated threat detection—achieve 73% accuracy in anomaly detection catching attacks SIEM systems miss.

Design biomimetic robots inspired by nature—implement gecko adhesion, swarm intelligence, and soft robotics using billions of years of evolution.

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.

Deploy high-performance computing with parallel processing and distributed systems—access supercomputer capabilities through cloud HPC for AI workloads.

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.

Detect AI-generated deepfakes with neural network analysis and authentication methods—combat misinformation with 73% accuracy detection models.