I built an AI system that rigorously separates what can be confirmed with solid technical evidence, what remains mere speculation, and what cannot be concluded beyond a certain point.
Please try the KYK public beta. If possible, I would like you to test its limits with tough, ambiguous, and demanding tasks.
Note: I am not a native English speaker. Please share your thoughts in writing so we can maintain a clear, verifiable record.
Today's Frontier AI narrative seems obsessed with the idea that humans and LLMs can stand shoulder to shoulder as peers. I disagree.
An LLM is, at its core, heavy machinery for excavating knowledge. To operate safely alongside humans, it demands industrial-grade safety measures—just as a real-world construction site requires safety fences, lockouts, and strict permission boundaries to keep human workers out of hazardous zones.
Without these explicit boundaries, LLMs will readily build elaborate castles nobody asked for.
LLMs are inherently lazy, arrogant, and yet surprisingly patient.
I am looking for architectural feedback. Where should human approval points and authority boundaries be placed?
The output generated by LLMs can look deceptively flawless, yet conceal a single line capable of catastrophic failure. It is smooth and polished—like a razor blade hidden beneath a layer of cream.
One naive engineering approach is to force the LLM to request approval at every single micro-step. But that is unworkable in production; it simply induces approval fatigue. It is like being required to call the bakery for permission before every single bite of cake. Nobody can build systems that way.
As an initial prototype, I have implemented this workflow. Moving forward, I want to foster open discussion around standardizing these validation frameworks.