Questions I keep returning to.

Most AI benchmarks judge intelligence by looking at the final answer: did the model solve the problem, pass the test, complete the task, or produce the expected output. That matters, but it is not enough. This is a research direction that asks a deeper question: what kind of internal process produced that answer? It started while studying Google DeepMind's March 2026 AGI measurement framework and hackathon, which break intelligence into cognitive abilities like learning, attention, reasoning, metacognition, executive function, and social cognition. The angle being explored here is that those abilities should not only be measured at the final output layer, but also inside the model's per token generation dynamics, the shape of the model's thinking may matter as much as the answer it gives. This is not a finished AGI test. It is a step toward measuring the internal structure of intelligence.

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I have been exploring a question that keeps showing up across everything I build: what should a database become when software, AI agents, users, files, code, memory, and validation all need to live together? Most databases are built around one shape of information, rows, documents, objects, vectors, graphs, or files, but modern AI native systems increasingly need many shapes at once, plus the ability to know where information came from, how it changed, why something matched, and whether it can be trusted. I am calling the current direction NexusDB. It is not finished, and I do not want to present it as if it is. This is an active research and architecture project, currently at the Genesis level. After about six months of research, the core evidence kernel has just come into focus.

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Freshman year college presentation on graphene, hemp derived carbon, and the relationship between light, quantum context, and high performance material properties.

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