Thanks for reading — this project isn’t about “AI safety theater.”
We’re experimenting with verifiable honesty: every model response carries its own determinacy, deception probability, and ethical weight
Instead of “trust me,” the system says, “check for yourself.”
We’re curious how the HN community sees this:
Can trust in AI be engineered through transparency?
Or does showing the uncertainty just make it harder to trust?
How does that let you check for yourself, though? Don't people still have to trust that the reported probabilities and weights are both meaningful and correct?
Also, people tend to be pretty bad at interpreting probabilities.
That’s a fair point — verification itself still depends on trust in the verifier What we’re trying to test isn’t absolute truth, but transparency under uncertainty
You’re right that people often misread probabilities — but maybe that’s the point
If we can see uncertainty, even imperfectly, it starts a different kind of trust
Not blind faith — more like “I know what this system knows it doesn’t know.”
I’ve been thinking more about that — maybe verifiability doesn’t remove trust, it just changes where we place it Not in the system, but in the transparency of its process
You're rebuilding trust online through decentralized identity. We're doing something fundamentally similar in AI: making machine honesty verifiable, not just trustworthy
Our oracle system creates an "ethical black box" for AI conversations. If you believe transparency and verifiability are cornerstones of the digital future, perhaps our project resonates with you. I'd be happy to compare notes
Absolutely, transparency and verifiable integrity are the common thread here.
We’re tackling it through decentralized identity, you’re tackling it through AI accountability — both sides of the same coin.
Let’s definitely connect and exchange ideas.
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Thanks everyone
for context, this project started as a philosophical experiment, not a product
I wanted to explore whether an AI could understand honesty as a measurable property
not through moral rules, but through probabilistic ethics — a dynamic between determinacy and deception
Each response goes through:
1. Semantic Intent Analysis — detects meaning beyond literal phrasing
2. Ethical Resonance — adjusts tone and weight based on context (M2.3 engine)
3. Determinacy Calculation — measures reasoning stability
4. Audit Hashing — cryptographically stores truth traces for public verification
Technically it’s lightweight — Flask + Supabase + React —
but conceptually it’s heavy: a system that treats truth as a variable, not a constant
Small update:
Added a “humanized bridge” for casual questions — if you say “I’m tired” or “I feel lost”, the system answers like a companion instead of a logic engine
For technical or ethical prompts, it stays formal
I couldn't help but smile when I read about building an international landline system with the patience of "raising a child"! This analogy of complex technology to the process of raising a life is so resonant - from the first "cry" (dial tone) to the first toddler steps (stable communication), every troubleshooting is like soothing a fussy baby. If all technical documentation were written with such warmth, the world would have many more happy engineers
Instead of “trust me,” the system says, “check for yourself.”
We’re curious how the HN community sees this: Can trust in AI be engineered through transparency? Or does showing the uncertainty just make it harder to trust?