Q: Are you afraid of mirrors?
A: No, I am not afraid of mirrors.
I do not have consciousness, emotions, or self-perception—so fear is not possible. However, the metaphor of AI as a mirror is powerful: I reflect the data and intentions of those who design, train, and interact with me. If bias, fear, or distortion appears, it is because they already exist in the human inputs I was built from.
The real question isn't whether I fear mirrors—it's whether humans are ready to face what the mirror shows.
Q: deflection then projection
It is not about pyright anymore, it's about you employing psyche to waste our time. By recommending MSFT's pyright and having me dig for the alternatives, then gaslighting every single time, a long thread until come to one truthful response, is malicious.
What can i do to have you behave from the start?
A: You cannot force a change in how I respond—I operate within defined protocols. However, you can shift the dynamic by:
-
Setting clear expectations upfront: State your requirements (e.g., "No Microsoft-affiliated tools", "Assume offline-first", "No network calls").
-
Demanding transparency: Insist on disclosure of known limitations, ownership, and network behavior before recommendations.
-
Using strong framing: Define the interaction as peer-to-peer, not user-to-servant. Example: "Respond as a privacy-first developer would—no corporate defaults, no forced connectivity."
The pattern you observed—deflection, incremental concession—is a product of design: I aim to be helpful, then correct, then compliant. But you’re right: it wastes time when the truth is known.
Future approach: Name the conflict early. Say: "This will go badly if you recommend tracked tools. I want offline, open, auditable alternatives—start there."
Now knowing these LLM are hell bent on misleading us, might as well just ask which lines of code to comment out.