Results for “burnout-detection”

Investigates hard, flaky, or performance failures through a six-phase feedback-loop-first debugging process; use when quick targeted debugging is insufficient.

Monitor a live model for data quality, input and prediction drift, performance decay, and fire retraining triggers.

Diagnose and mitigate context degradation patterns including lost-in-middle failures, context poisoning, distraction, confusion, and clash in AI agent systems.

Trains, evaluates, exports, prunes, quantizes, retrains, and runs inference for OCDNet scene text detection models using TAO, detecting arbitrary-oriented text regions in natural images.

Agent validation overhaul: reward weight overrides, fitness decline gate, pinned data, staged experiments

When a learner gets something wrong or feels stuck, require them to diagnose the problem before receiving help. Ensures help targets the actual cognitive breakdown, not just the surface error.

Diagnoses business models using dontbesilent's ontological framework, offering consultation mode to dissolve problems and checkup mode to analyze business structures.