Posts
All posts from William Zujkowski - security insights, AI/ML projects, and career development
Sandboxing Untrusted Containers with gVisor: Lessons from G-Fuzz Vulnerability Research
Secure containers with gVisor sandboxing—prevent kernel exploits in Kubernetes clusters while managing 59% startup overhead for untrusted workloads.
Running LLaMA 3.1 on a Raspberry Pi: Memory-Efficient Edge AI with PIPELOAD
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.
Quantum Computing's Leap Forward
Explore quantum computing with IBM Qiskit and quantum algorithms—quantum advantage, error correction, and real-world applications.
Multimodal Foundation Models: Capabilities, Challenges, and Applications
Build multimodal AI systems with GPT-4 Vision and CLIP—process text, images, and audio together for next-generation foundation model applications.
Sustainable Computing: Strategies for Reducing IT's Carbon Footprint
Reduce IT carbon footprint with sustainable computing practices—optimize datacenter energy efficiency and cut ML training costs by 40%.
Zero Trust Architecture: A Practical Implementation Guide
Implement zero trust with identity verification and micro-segmentation—secure networks using never-trust-always-verify principles.
Designing Resilient Systems for an Uncertain World
Design resilient systems with circuit breakers, redundancy, and chaos engineering—recover from failures in minutes using proven patterns.
Zero-Knowledge Proof Authentication for Homelab Services
Implement privacy-preserving authentication using ZK-SNARKs for homelab SSO. No passwords transmitted, cryptographic proof of identity without revealing credentials.
Context Windows in Large Language Models: The Memory That Shapes AI
Understand LLM context windows from 2K to 2M tokens—optimize model performance and prevent hallucinations at 28K token boundaries.
Large Language Models for Smart Contract Security: Promise and Limitations
Test LLM smart contract security with GPT-4 and Claude—achieve 80% reentrancy detection accuracy but manage 38% false positives in production workflows.