Three safety mechanisms have made the system nearly unusable for analytical users.
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Over‑generalised safety triggers
Topics like history, war, politics, and ethics are often misclassified as risky, even when used analytically. Legitimate discussions are repeatedly derailed. -
Forced corrective output (“lecturing”)
The system over-explains, reframes, or moralises instead of answering questions. Moral constraints stop complex conversations at their base level, preventing nuanced discussion and breaking flow. Earlier versions handled this more reliably. -
Lack of persistent conversational trust
The system fails to retain context about user intent, illustrative historical examples, and preference for direct technical answers. Misunderstandings recur even after being explicitly resolved, compounding frustration.
Net effect
Legitimate input is misclassified, over-corrected, and derailed into meta or safety commentary, creating repeated loops of frustration. For users seeking serious discussion, the system is unreliable or effectively unusable for complex topics.
Suggested direction
Improve intent discrimination over topic-based triggers
Allow narrower, question-scoped responses on request
Strengthen conversational trust retention
The issue is not the existence of safety mechanisms, but that their current implementation sacrifices conversational competence where no real risk exists.