Raw LLM Responses

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G Yes, but once all cars are self-driving and all pedestrians are replaced with la… rdc_f6y7bqa G If human hackers can get into our systems what says that AI cannot hack into the… ytc_UgwGKR1TL… G ‘Ai’ is what I say about ai “AIIII!! AAAIIIIIII!!! AAAAIIIII!!!! “ (*the sound o… ytc_UgzeteZh8… G An interesting thought that I can’t find any statistical evidence regarding. Pe… rdc_gsoxty1 G Those are bad arguments but an actually good one is that US defense spending has… rdc_dcwtnx3 G im an artist that can proudly say that sometimes the ability to draw isnt natura… ytc_UgwY9754o… G So the press conference DT held in the oval office back in February with an AI t… ytc_UgxzXRq-D… G Art has never been accessible though. Until very recently, the raw materials wer… ytr_Ugzi2uq0q…
Comment
Hallucinations happen when the conversation begins to represent too many different concepts, the LLM has to integrate the entire past conversation to keep track of what it said in the past. To solve the problem of excessive input to the LLM summarizers are used to consolidate the past conversations, but this approach begins to distort the past conversation as the summarizers leave out details. The effect ends up causing the LLM to respond incoherently, or repeating itself. There is way to prevent that and make the LLM more robust in dealing with shifts in context of a conversation and coping with detail loss from summarizer's. given that, achieving AGI is possible, particularly when Mixture of experts architectures are used.
youtube 2025-11-14T15:5…
Coding Result
DimensionValue
Responsibilitynone
Reasoningunclear
Policyunclear
Emotionindifference
Coded at2026-04-26T23:09:12.988011
Raw LLM Response
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