The goal is not to manufacture certainty. It is to turn broad curiosity into questions that can be examined, challenged, and revised. Each path begins with a concrete problem and keeps a record of what changed along the way.
Study hard what interests you the most in the most undisciplined, irreverent and original manner possible.
Study hard what interests you the most in the most undisciplined, irreverent and original manner possible.
Feynpath treats each note as a continuation of work left by Turing, von Neumann, Hopper, and the builders around them.
Each agent experiment inherits an older discipline: separate procedure, evidence, and judgment before asking a machine to act.
What changes when modern AI keeps its claims tied to evidence instead of performing certainty?
Microsoft’s MDASH reached 88.45% on CyberGym. The next eleven weeks showed why cybersecurity’s durable AI advantage will live in open agents, tools, benchmarks, and proof, not in any single model.
Google Quantum AI taught a reinforcement learning agent to tune a quantum processor from its own error detection events, without stopping the computation. It scales because the problem is local.
AI is lowering the barriers to creation, and the research says it is also lowering how much we think while we create. The answer is not to refuse the tools. It is to design harder problems.
A reflection on the need to focus on doing things well instead of doing them fast, especially in a time that pushes us to produce without stopping.
The next entries will continue the Bayesian agent experiment, develop the post–von Neumann reading series, and turn promising questions into prototype write-ups. Each update will link methods, limitations, and artifacts when they are ready, while older notes remain open to correction.
Browse every research note