Project Prometheus: Teaching AI Evolution Through Interactive Systems

Project Prometheus began from a simple frustration: most explanations of AI progress are either too shallow to teach anything or too technical to invite exploration. The field moves through discontinuities: rules give way to learned representations, learned representations scale into transformers, transformers become tool-using agents, and agents begin to look less like chat surfaces and more like operating systems.

The project turns that arc into an interactive simulation. Rather than presenting AI history as a lecture, Prometheus gives the user a navigable model of capability progression: what changed, why it mattered, what new behaviors became possible, and what tradeoffs appeared at each stage.

The Design Thesis

AI education works better when the learner can compare systems side by side. A static essay can describe the difference between a rule-based system and a transformer, but an interactive environment can make the gap feel concrete. It can show why brittle symbolic rules struggle with ambiguity, why neural networks generalize differently, and why transformer-era systems unlocked a new design space for language, planning, and multimodal work.

Prometheus is built around that comparative frame. Each stage of the timeline is a design object: inputs, capabilities, limitations, interaction pattern, and representative applications. The point is not to predict a grand destiny for AI. The point is to give people a clear mental model for how one capability layer creates the conditions for the next.

Why It Belongs in the Portfolio

The rest of this portfolio is mostly about building AI systems: memory, voice, agent harnesses, research scoring, and local interaction loops. Prometheus sits beside that work as the explanatory layer. It translates technical progress into something legible for students, executives, product teams, and curious builders.

That makes it a useful complement to the deeper engineering posts. AgentEvolve asks when orchestration helps. Engram asks what memory should mean. PersonaPlex asks how people should supervise real agent work. Prometheus asks how to teach the shape of the whole field without sanding off the important details.

Current Status

Prometheus is a live interactive project focused on AI capability progression, model comparison, and education through simulation. The next useful step is to expand it from a timeline into a set of scenario-based comparisons: what different generations of AI systems do when given the same ambiguous task, how they fail, and what additional scaffolding makes them useful.