Deep dives into AI, systems architecture, and modern technology
A comprehensive deep dive into the foundations, constraints, and real-world implications of large language models. From architectural basics to alignment challenges, this series establishes the minimum shared technical vocabulary needed to reason accurately about modern LLM systems.
Establishing the minimum shared technical vocabulary required to reason accurately about modern large language models. From RNNs to Transformers, understanding the architectural foundations.
Read ArticleUnderstanding scaling laws, emergent capabilities, and why architecture alone doesn't explain modern LLM performance. When size fundamentally changes behavior.
Read ArticleMoving beyond standalone models to production systems. RAG, vector databases, MoE architectures, and why deployment is a distributed systems problem.
Read ArticleRLHF, Constitutional AI, emergent behavior, and the unresolved questions that define the next phase of LLM research. Where certainty gives way to trade-offs and open problems.
Read ArticleDrawing from years of managing enterprise SOCs and building security programs for ultra-high-net-worth individuals, this series examines how threat actors adapt their methodology based on target type while maintaining consistent objectives. Learn the strategic defense framework that translates between enterprise and personal security contexts, from threat landscape analysis to operational resilience under the assume breach model.
How threat actors target enterprises versus high-value individuals, building defensible architecture for both contexts, and operating under the assume breach model. From enterprise tooling to personal security stacks that scale.
Read ArticleOperational implementation of assume breach: detection strategies that compress timelines, containment techniques that minimize blast radius, and recovery processes that build long-term resilience. Security as operational discipline.
Read ArticleNew articles and series in development. Check back soon!
