Raw LLM Responses
Inspect the exact model output for any coded comment.
Look up by comment ID
Random samples — click to inspect
G
Isn't it clear that these peopIe are using the bible as a blue print to end the …
ytc_Ugw5PDfcr…
G
Insane take: Output does not match skill and pumping out AI code is not widening…
ytc_UgwBXkk3Q…
G
You'll never know when they start using AI for the news or for political TV, it …
ytc_UgxOhXh-Y…
G
Looking forward to when AI will take over most of the private equity bros haha…
ytc_UgyQSKGF9…
G
AI can not have original thoughts it can only pull from what it can find on the …
ytc_Ugzd0Cw7A…
G
as long as the art looks good, I honestly don't care how it was made, because be…
ytc_UgzivLHz-…
G
1:06:00 👀👀👀👀 I guess the idea is too preposterous for her to even scratch the su…
ytr_UgyUIk1nd…
G
Yeah i agree with this but that does not mean we should not learn coding. We can…
ytr_Ugzadic6M…
Comment
By implementing logical rules and conditions, AI could have acted as an automated watchdog:
1. Preventative Controls:
• If a transaction exceeds a certain threshold, then it requires dual approval.
• When an approval chain is bypassed, flag it for review.
• How does this compare to normal transaction patterns?
2. Detection & Investigation:
• If a pattern of suspicious transactions emerges,
• Then trace them back to the decision-makers,
• When inconsistencies appear, cross-check supporting documentation,
• How does this align with past fraudulent cases?
By embedding these logic-based safeguards, AI could have eliminated loopholes before they were exploited. But even with strict rules, human manipulation can still find ways around them. From your experience with DOGE, would AI have been enough to stop fraud entirely, or would people always find creative ways to circumvent the system?
youtube
AI Governance
2025-10-03T10:2…
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | developer |
| Reasoning | consequentialist |
| Policy | regulate |
| Emotion | indifference |
| Coded at | 2026-04-27T06:24:59.937377 |
Raw LLM Response
[
{"id":"ytc_UgxxQYlsZymChyVw19t4AaABAg","responsibility":"ai_itself","reasoning":"mixed","policy":"none","emotion":"mixed"},
{"id":"ytc_UgzKz_7QdsMw_OfnPGR4AaABAg","responsibility":"distributed","reasoning":"mixed","policy":"none","emotion":"mixed"},
{"id":"ytc_UgyLxljpKEfbwm3B5gt4AaABAg","responsibility":"unclear","reasoning":"deontological","policy":"unclear","emotion":"resignation"},
{"id":"ytc_UgzieOth2nDrY3_b2DR4AaABAg","responsibility":"ai_itself","reasoning":"consequentialist","policy":"none","emotion":"fear"},
{"id":"ytc_UgyKftSaUAOWRJ0fmXJ4AaABAg","responsibility":"company","reasoning":"unclear","policy":"none","emotion":"outrage"},
{"id":"ytc_UgyI2fuvUomiOXgKtvV4AaABAg","responsibility":"ai_itself","reasoning":"consequentialist","policy":"none","emotion":"fear"},
{"id":"ytc_UgxYqsltqBFOq5ZfVwB4AaABAg","responsibility":"government","reasoning":"deontological","policy":"regulate","emotion":"outrage"},
{"id":"ytc_UgyoLefoh89ONUBz1Kd4AaABAg","responsibility":"creator","reasoning":"deontological","policy":"regulate","emotion":"approval"},
{"id":"ytc_UgyNPWXW_pBeF9NibBF4AaABAg","responsibility":"developer","reasoning":"consequentialist","policy":"regulate","emotion":"indifference"},
{"id":"ytc_UgyOCJg43TEcZa_mkR54AaABAg","responsibility":"developer","reasoning":"mixed","policy":"regulate","emotion":"mixed"}
]