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twerkmeister

The data set quality and much of the methodology is just as abysmal as the writeup... as pointed out in my second post: https://www.kaggle.com/competitions/kaggle-measuring-agi/dis...

So it must have been the "results" that moved the needle :D

I updated my post title to reflect the original title and send a mail to the mods to restore the original title. Lets see. I was not aware of the editorialized title rule otherwise I had updated my post before linking it here.
What do you think my two long posts in the discussion forum of kaggle are? I am the author and the evidence I present very much justified the original title.
They also host the arc prize challenge with prices up to 850k https://www.kaggle.com/competitions/arc-prize-2026-arc-agi-3...
I am the author of the linked article, so you say my opinion should be allowed in the title?
They also removed the fact it was a competition hosted by a prestigious AI lab
Please note this Post was just renamed without my involvement from:

Blatant AI slop just won a 25K USD Deepmind Kaggle Grand Prize

into

"Evidence of inconsistencies in evaluate process and selection of winners"

That's fair and your good right. However know that my frustrations stems from spending two and a half days feeling like in the story of the emperors new clothes digging into this shit that was made king by a number of employees of one of the most prestigious AI labs.

Show HN: Tokenflood – simulate arbitrary loads on instruction-tuned LLMs

https://github.com/twerkmeister/tokenflood

Hi everyone, I just released an open source load testing tool for LLMs:https://github.com/twerkmeister/tokenflood=== What is it and what problems does it solve? ===Tokenflood is a load testing tool for instruction-tuned LLMs hat can simulate arbitrary LLM loads in terms of prompt, prefix, and output lengths and requests per second. Instead of first collecting prompt data for different load...

( Read More... | no comments | 4 points | on HN )