Blog

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.

How Handwright turns a scanned worksheet into a custom .ttf font — OpenCV glyph extraction, potrace vectorization, and fonttools assembly. Local-first, no cloud required.

How I built Tsundoku — a curated digital bookshelf with multi-source enrichment, free reading links, and a static-site architecture that serves 3,500+ books without a database.

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.

Learn to build and secure a production-grade private cloud using Proxmox VE. Covers network segmentation, backup strategies, security hardening, and resource management with real homelab implementation lessons.

Eight security layers that stopped real attacks in homelab testing: minimal base images, user namespaces, seccomp profiles, network segmentation, and more. Defense-in-depth without Kubernetes overhead.

Real-world guide to monitoring security events in your homelab. Covers Prometheus configuration, Grafana dashboards, and alerting rules for threat detection.

NodeShield enforces SBOMs at runtime using CBOM policies to prevent supply chain attacks. Homelab Docker deployment guide with attack simulations, 98.3% prevention rate, and <1ms overhead.

Deploy PromSketch to optimize slow PromQL queries using sketch-based approximation. Homelab benchmarks show 2-100x speedup on percentile queries.

Google's Willow chip achieved the first quantum error correction breakthrough below the critical threshold, proving that adding more qubits can actually reduce errors. This changes the future of computing, cryptography, and AI forever.

Deploy and benchmark Wazuh and Graylog SIEM solutions in your homelab. Performance analysis, resource usage, and integration patterns for security monitoring.

Implement post-quantum cryptography with CRYSTALS-Kyber and Dilithium—prepare homelab for quantum threats using NIST-approved algorithms.

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

Build automated security scanning pipelines with Grype, OSV, and Trivy—integrate vulnerability detection into CI/CD workflows with actionable reporting.

Build Proxmox high-availability clusters with shared storage and automated failover—implement live migration for zero-downtime homelab maintenance.

Test IoT security with OWASP IoTGoat—practice firmware extraction, API exploitation, and hardware hacking in secure lab environments.

Prioritize vulnerabilities with EPSS and CISA KEV catalog—move beyond CVSS scores to risk-based patch management using exploitation probability metrics.

Build MITRE ATT&CK threat intelligence dashboard with Python—track adversary tactics and techniques using open-source threat feeds.

Implement zero trust with VLAN segmentation—secure homelab networks using micro-segmentation and layer 3 firewalls for defense in depth.

Migrate to self-hosted Bitwarden—deploy secure vault with backup strategies, SSL certificates, and database encryption for full control.

Deploy Suricata IDS/IPS for real-time network threat detection—configure rule management, performance tuning, and SIEM integration for homelab monitoring.

Harden Docker containers using AppArmor and SELinux for isolation without orchestration overhead. LSM profiles, seccomp filters, and capability dropping at homelab scale.

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.

Build enterprise vulnerability management with open source—deploy scanning, remediation tracking, and compliance using Nessus and OpenVAS.

Deploy DNS-over-HTTPS with Pi-hole and dnscrypt-proxy—encrypt DNS queries for home network privacy and prevent ISP monitoring with DoH implementation.

Implement eBPF security monitoring for real-time kernel visibility—track syscalls and network activity with production-ready patterns for threat detection.

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.

Build security-focused homelab with Proxmox, VLANs, and IDS/IPS—create testing environment for cybersecurity and family data protection.

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

Navigate IT support to senior InfoSec engineer path—learn from 15+ years securing federal systems with practical career transition advice.

Build Raspberry Pi security projects with Pi-hole, VPN gateway, and honeypots—deploy practical network monitoring and threat detection on budget hardware.

Master continuous cybersecurity learning with lab exercises, research tracking, and community engagement—stay current without burnout.

Automate home network security with Python and Ansible—deploy patching, threat detection, and compliance scanning for homelab infrastructure.

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

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

Build privacy-respecting sites with Eleventy—create fast, accessible static websites with zero tracking and excellent Core Web Vitals.

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

Deploy blockchain beyond cryptocurrency with Ethereum and smart contracts—build decentralized trust for supply chain and identity verification.

Secure containers with gVisor sandboxing—prevent kernel exploits in Kubernetes clusters while managing 59% startup overhead for untrusted workloads.

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.

Explore quantum computing with IBM Qiskit and quantum algorithms—quantum advantage, error correction, and real-world applications.

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

Reduce IT carbon footprint with sustainable computing practices—optimize datacenter energy efficiency and cut ML training costs by 40%.

Implement zero trust with identity verification and micro-segmentation—secure networks using never-trust-always-verify principles.

The cascade failure that changed how I think about building systems that break gracefully.

Implement privacy-preserving authentication using ZK-SNARKs for homelab SSO. No passwords transmitted, cryptographic proof of identity without revealing credentials.

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.

A Saturday afternoon coding project that taught me more about assumptions than algorithms.

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.

Prepare for quantum computing threats with post-quantum cryptography—protect RSA and ECC encryption from quantum attacks using NIST-approved algorithms.

Implement quantum-resistant cryptography with NIST post-quantum algorithms. Future-proof encryption against quantum attacks using Kyber and Dilithium.

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.

Deploy zero trust security with continuous verification and identity-centric controls—implement never-trust-always-verify for Federal EO 14028 compliance.

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.

Execute cloud migration from on-premises infrastructure with AWS/Azure strategies—reduce costs by 40% and improve scalability with proven patterns.

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.

Automate vulnerability detection in your homelab using Python and the National Vulnerability Database API. Track CVEs, scan dependencies, and integrate with monitoring systems.

Learn cryptography fundamentals with AES-256, RSA, and SHA-3—implement encryption, hashing, and digital signatures for production security systems.

Master secure code development with input validation, parameterized queries, and secrets management—prevent SQL injection and XSS in production systems.