Today Vaibhav sooryavanshi can win 5 different trophies -
Super striker of the season - highest strike rate
Most sixes in season - 72 sixes
Most valuable player award (competition with Rabada}
orange cap winner (if Sai and Gill fails)
Emerging player of the Season
3 confirmed
@nk777gaming@Melbourne__82 Cricket is a team game and no matter how much you score, if the team fails you, u can't win it. Kohli lost even after 973 season. He played according to what he does always and he don't need to change what he do just for that. It is his identity and he should play like that
@Abhishe74899601 it is just about bad execution from the Rajasthan bowlers, Holder , Rabada , Siraj and prasiddh used the pace of the pitch better and got the results which Rajasthan bowlers failed to do and thus the result
@VivekPrasadMou1@filmfare I am not saying Ranveer Singh is wrong here but backing out at last moment before the shooting begins when producer has already spent crores of rupees is bad for the business. I mean it is kind of unprofessional behavior which is bad in every field .
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@Brutu24@ImRohanGulavani Pant is a bad player of offside but Vaibhav is not.. Along with abhishek sharma, he can play well on the offside. vaibhav plays cut shots, mid wicket and other offside areas. Watch his innings of 93 of 38 deliveries. He hit clean sixes on the offside
@Brutu24@mipaltan bro just watch his innings of 93 of 38 deliveries in the last match. Most of his shots were on offside .. what cricket you are watching man..
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the worst prompt engineering advice starts with
“act like Einstein”
“pretend you are Steve Jobs”
“behave like a 10x engineer”
LLMs don’t really care about that stuff
what actually matters is how well you specify the problem.
good prompting is usually just clearly specifying:
• the task
• relevant context
• constraints
• expected format
• examples of good output
EXAMPLE:
You are an ML researcher.
Task:
Explain transformers to a CS student.
Constraints:
- concise
- technical
- include equations
- avoid analogies
Output:
Markdown
transformers are extremely sensitive to context and structure
changing the framing, ordering, constraints, or even a single example can completely alter the model’s reasoning and final output.
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