Maintainer: data will not scale; the decision engine is an AI classifier, not ML,
with local memory, DSPy style. In-house on the metered client; retrieval by signal
profile re-ranked by code embeddings; compiled (bootstrap few-shot, instruction
search) per training fold; retrieval never crosses the evaluation boundary;
confidence calibrated on held-out repositories. Design and tasks 6+ to revise.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Maintainer: hard-coded application specifications are problematic; WordPress is
not the only PHP application, same for Python and JS. Language-level knowledge
stays; framework roles are inferred per repository; existing framework entries
become tagged optional priors kept only by A/B evidence; leave-one-framework-out
evaluation.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Maintainer: the tool will never run against the test cases it is trained with;
its purpose is a repo safety net. Repository-grouped folds, repository-agnostic
features, BLOCK/ADVISORY operating points for changed code, out-of-repo
calibration, adaptation kept separate.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Maintainer direction: A/B testing to extrapolate detection instead of hard-coding
it, and a trained decision engine. Instruments become signal producers; labels
from ground truth with provenance; a simple calibrated engine with population
folds; experiments as plane Runs; extrapolation with intervals, checked once on
frozen v3; the engine is what scan/pre-push/plane report.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
