Why I asked. I wanted earlier experiments to improve later advice. I had been thinking about what an assistant should remember as its models became more capable.
What should I preserve so a more capable future assistant could learn from earlier experiments?
Preserve the evidence available at the time, the options considered, what was tried, and the observed result. A later model could revisit those decisions instead of inheriting a summary with its uncertainty stripped away.
What if I had recommendations but had never checked whether they worked?
Each experiment needed a hypothesis, a measurement window, and a follow-up tied to the actual change. It also needed separate outcomes for success, failure, inconclusive evidence, and broken measurement.
What feedback could I give to make the loop useful?
Rate whether the advice was useful, record what was actually changed, and later review the result. A positive rating was feedback about a recommendation; it was not evidence that the experiment had succeeded.
I learned that a useful memory needed links from an idea to an action, an observed result, and a later decision. Saving more notes did not close that loop, and missing measurements could not be treated as failed experiments.
I came to see learning as a change in the next decision that could be traced to evidence from the last one.
“Help me design a learning loop for an AI assistant: what should it record before an action, what should it measure afterward, and how should uncertain results affect later advice?”
Why I askedI wanted to understand how speed, energy use, and a changing range estimate related to each other.
What I learnedI learned that air resistance rose quickly at highway speeds. Lower speeds could extend range, but the gain depended on conditions. A steady arrival estimate could already reflect adaptation to lower energy use.
Why I askedI wanted to understand what isolated desktops actually solved. I followed the idea through the choice of hardware and operating system.
What I learnedI learned to separate isolation, capacity, supervision, and scheduling. Two agents could have separate desktops and still conflict while changing the same website account.
Why I askedI had assumed a classifier's main job was filtering information before a larger model saw it. I wanted to understand what changed when software owned the workflow.
What I learnedI learned to separate model judgments from the code that stored state and chose actions. A more detailed prompt still left the model in charge; explicit state and rules made parts of the process inspectable and testable.