Edge intelligence, central reasoning
A research note on giving efficient local models and larger central systems different jobs inside one security architecture.
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Technical articles, research, engineering notes, and professional analysis about intelligent systems and the real engineering required to make them useful.
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Ergodic Theory: What the Long Run Can Teach Us
Ergodic theory explains when one long trajectory can reveal a system’s statistical truth—and why data volume alone cannot guarantee learning.
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articleFrom the law of large numbers to Mamba-based telemetry analysis and human trial-and-error, the value of repetition depends on what the process lets us observe.Ergodic theory explains when one long trajectory can reveal a system’s statistical truth—and why data volume alone cannot guarantee learning.
articleRyan Powers reflects on his path from Marine cyber operations to cybersecurity graduate study and applied research at USF.How military service, resilience, and an applied research mindset shape my work in cybersecurity and AI at the University of South Florida.
articleA practical guide to tokenization and embeddings, covering BPE, WordPiece, Unigram, SentencePiece, word2vec, GloVe, fastText, contextual Transformers, and modern retrieval models.Tokenization breaks text into model-readable units. Embeddings place those units—or entire passages—into learned numerical spaces. Here is how the major approaches work and why the distinction matters.
articleLearn the foundations of reinforcement learning and Markov decision processes, then compare PPO and SAC through their mechanics, use cases, strengths, weaknesses, and practical tradeoffs.A practical guide to two leading reinforcement-learning algorithms: how Markov decision processes frame the problem, why PPO favors stable updates, and why SAC emphasizes sample efficiency and exploration.
articleA practical guide to agentic engineering for DevOps practitioners, covering harness, context, prompts, RAG, memory, tools, control loops, sandboxes, guardrails, observability, and evaluation.The model may supply the reasoning, but the harness makes an agent operational. Here is how prompt, context, runtime, tools, memory, safety, and observability fit together—and why the stack should feel familiar to DevOps engineers.
articleA practical guide to the difference between agents, skills, and AGENTS.md—including how definitions, tools, permissions, and reusable workflows fit together in Codex.Agents provide judgment, context, and action; skills package repeatable methods. Understanding the boundary makes both easier to design, secure, and reuse.
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Ergodic Theory: What the Long Run Can Teach Us
From the law of large numbers to Mamba-based telemetry analysis and human trial-and-error, the value of repetition depends on what the process lets us observe.