Summary: The series "Saving Grace" from TNT who are behind the other great series "The Closer" has a great soundtrack to it from Everlast. While it lasts here it is on YouTube sorry GoogleVideos... : The series stars Holy Hunter and is one of the recent 'original' series that survived the Hollywood scenario strike and rightly […]
The series "Saving Grace" from TNT who are behind the other great series "The Closer" has a great soundtrack to it from Everlast. While it lasts here it is on YouTube sorry GoogleVideos... :
The series stars Holy Hunter and is one of the recent 'original' series that survived the Hollywood scenario strike and rightly so. Even if it can be considered gritty and one can question the need for so much boozing and smoking to give a realistic shine, it is a great show !
The heroine’s strong character can be an addictive initiation but her troubled and unconventional relationships have her leading a pretty solitary existence even though she may be surrounded by friends. It’s only when the story unravels that you realise that her torments are well and truly a dark burden that would drown ‘many a man’, resonating her strength and determination, not just a self pitying and lost soul.
The song that you can listen to in the video clip above is really successful in providing the series with a 'soul' !
Extract from lyrics :
One time around the block
Two times around the clock
Three times don't cross the little lady
So pretty, and oh so bold
Got a heart full of gold on a lonely road
She said I don't even think that god can save me
...
One time around the sun
Another year older and my work ain't done
It's time for me to write the final chapter
Deal the cards, and roll the dice
Sex, drugs and rock & roll are my only vice
Tryin to figure out just what's hereafter
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I question the reliability and accuracy of Generative AI (GenAI) in enterprise scenarios, particularly when faced with adversarial questions, highlighting that current Large Language Models (LLMs) may be data-rich but lack in reasoning and causality. I would call for a more balanced approach to AI adoption in cases of assisting users, requiring supervision, and the need for better LLM models that can be trusted, learn, and reason.
