Yohei Nakajima Portfolio
Created BabyAGI in March 2023, widely credited as one of the first popular autonomous agents to use an explicit task-creation-and-prioritization loop for open-ended goals; the project (18,000+ GitHub stars) helped popularize the now-standard agent pattern of maintaining a task list and re-planning after each step.
What makes it work
A breakdown of the choices that make this portfolio stand out.
A minimal, explainable reference architecture
BabyAGI's core loop (execute, create new tasks, prioritize, repeat) is deliberately small enough to read in one sitting, which is why it became a widely forked teaching example for the task-planning agent pattern rather than a black box.
Public build-in-public documentation alongside the code
Writing about experiments as they happen, rather than only publishing finished repositories, shows the reasoning behind design decisions, not just the resulting code.
Applying agent design to a concrete domain problem
A tool applying the same planning-and-feedback agent pattern to a specific, well-scoped use case (pitch practice for founders) shows the underlying architecture generalizes beyond the original demo.
What AI Agent Architects can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Keep a reference agent architecture small enough that someone can read the entire control loop in one sitting — that's what makes a pattern get forked and taught.
Document your experiments in public as you go, not only the finished repository — the reasoning behind a design is as instructive as the design itself.
Show your general agent pattern applied to at least one concrete, narrow use case — it proves the architecture generalizes rather than only working in a demo.
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