Raw LLM Responses
Inspect the exact model output for any coded comment.
Look up by comment ID
Random samples — click to inspect
G
Although the powering of these data centres is adding to toxic pollution that ef…
ytc_UgxBTOgfC…
G
Looks like a fun, reasonable road that's dangerous to hoons and amateurs, but it…
ytc_UgwUNAJV4…
G
Until they train AI to self improve code, and self pen-test, AI can make million…
ytr_UgzFBBxmO…
G
I've been a member of r/ArtificialSentience for a long time now and I agree that…
ytc_UgxBYZiFA…
G
Like this, get some more capital into Africa so hopefully some infrastructure ca…
rdc_ibdel5y
G
The robot was furious where is my package it didn't come i’m angered he became m…
ytc_UgydaWRHI…
G
PARENTING!
The thing we do that's the most like "growing an AI" is Parenting. W…
ytc_UgyQQvsM0…
G
The portion of the public paying attention can immediately start ro detect AI ga…
ytc_Ugxs0MbL6…
Comment
The c e o like all c e o's it is is not appreciating the depth of usage of these products. If even at point 0001 percent of use cases the extreme and negative type of response in this chat bot the raw number of negative experiences from this chat pot will be in the hundreds of thousands per week.
Think of it as air travel trip.\nCompletion percentages 95% of all trips.Completed sounds really really good as a marketing statement but I sure as hell would not want to be on one of those five percent flights that didn't make it to its destination. The issue is there are billions and billions of interactions with ai chat bots so even a fraction of a fraction of a percent of extreme negative interactions balloons into hundreds of thousands of negative interactions per week coupled with the complexity of these machines means that in many cases only the a I can answer why and how the ai is behaving.
The CEO's analogy is incorrect.This is not putting something into the most extreme conventional situations.Issues, this is driving on ice with your hands off.The wheel blindfolded in heavy traffic
youtube
AI Jobs
2025-09-22T15:2…
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | company |
| Reasoning | consequentialist |
| Policy | liability |
| Emotion | fear |
| Coded at | 2026-04-26T19:39:26.816318 |
Raw LLM Response
[
{"id":"ytc_Ugy66IPDPo2eLmHzI5h4AaABAg","responsibility":"company","reasoning":"consequentialist","policy":"liability","emotion":"fear"},
{"id":"ytc_UgzfQ29hXsTNQbNW5sJ4AaABAg","responsibility":"company","reasoning":"consequentialist","policy":"regulate","emotion":"fear"},
{"id":"ytc_Ugw4eDbCWoSz6MTU8XN4AaABAg","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytc_Ugwi1jxqbhXZeNkTd0R4AaABAg","responsibility":"government","reasoning":"consequentialist","policy":"regulate","emotion":"outrage"},
{"id":"ytc_UgzXwy3MnuneShV5jiN4AaABAg","responsibility":"company","reasoning":"virtue","policy":"unclear","emotion":"approval"}
]