FPF Reference

    The Bitter Lesson Stance

    Preface node heading:the-bitter-lesson-stance:882

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    FPF also carries a Bitter-Lesson-compatible stance. In AI, software, and open-ended engineering, systems that can use more search, more data, more compute, and more general learning often outperform brittle hand-coded procedure scripts when the domain changes or scale grows.

    FPF does not turn that observation into blind automation. It translates it into an architectural preference:

    • state goals, constraints, budgets, and checks more clearly;
    • give agents and teams freedom to search within those declared bounds;
    • keep safety, evidence, assurance, and gate conditions explicit;
    • measure outcomes and refresh policies when the environment or model changes;
    • avoid hiding brittle procedure scripts inside prose that looks like general guidance.

    The important separation is between design-time constraints and run-time action. A designer may declare prohibited actions, risk budgets, cost ceilings, allowed tools, escalation conditions, evidence minima, or acceptance criteria. That is different from prescribing every step the acting system must take.

    There are cases where procedure is required: safety, regulation, legal compliance, reproducibility, and training may need specified method descriptions. FPF does not forbid that. It requires the kind of claim to be explicit. A procedure script is a method description or work instruction; a constraint set is not the same thing; a monitor is not the same thing as evidence of success; a gate is not the work itself.

    This stance helps with human and AI work alike. A team can use general agents, search, simulation, model refresh, or state-of-the-art harvesting without surrendering safety. The freedom lives inside constraints, budgets, evidence, and typed checks.

    Last Updated: 2026-06-17 — upstream FPF commit 646b0b9b (github.com/ailev/FPF)