Edge intelligence, central reasoning
A research note on giving efficient local models and larger central systems different jobs inside one security architecture.
Insights
One publication. Many difficult questions.
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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| Title | Format | Topics | Published | Read time |
|---|---|---|---|---|
| Ergodic Theory: What the Long Run Can Teach Us | article | Research, Applied Research, Engineering | 9 min | |
| ML-KEM: The Post-Quantum Upgrade Hiding Inside the Handshake | article | Cybersecurity, Technical Strategy & Leadership | 7 min | |
| Edge intelligence, central reasoning | paper | Research, Cybersecurity, AI & Agents | 9 min | |
| Built to Serve: My Research at USF | article | Cybersecurity, Technical Strategy & Leadership, Applied Research | 4 min | |
| Tokenization vs. Embeddings: From Text Pieces to Meaning | article | AI & Agent Systems, Engineering | 10 min | |
| PPO vs. SAC: A Practical Guide to Reinforcement Learning | article | Applied Research, AI & Agents | 13 min | |
| Agentic Engineering Through a DevOps Lens | article | AI & Agent Systems, Engineering, Infrastructure & Platforms | 12 min | |
| Agents and Skills: The Worker and the Playbook | article | AI & Agent Systems | 8 min |
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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.