Of course, that’s your contention. You’re a first-year machine-learning engineer. You just got finished readin' Attention Is All You Need, probably watched a Karpathy video too. So now you’re convinced everything is just transformers and scaling laws.
That's gonna last until next month when you discover convolution, and then you're gonna be talkin' about inductive biases and locality and how CNNs were actually incredibly compute-efficient for vision.
Then you're gonna read the FlashAttention paper, and suddenly everything's about IO complexity and SRAM and how FLOPs don't matter because the whole goddamn thing is memory-bandwidth bound.
That'll last until somebody shows you an MoE model, and then you're gonna be in here regurgitating DeepSeek, talkin' about expert parallelism and active parameters and how dense models are economically obsolete.
Then six months from now you'll write one shitty Triton kernel, look at an Nsight trace for the first time, and start telling everybody Python isn't actually the bottleneck because the GPU is asynchronous.
And by next year you're gonna be standing right here explaining to me that your 400-billion-parameter model is fast because you turned on CUDA graphs.
In 24 hours I’m going to give one random person that retweets this tweet $10,000 in Bitcoin! (Yup, gonna experiment with this instead of cash haha) Make sure you follow me so I can dm you if you win :)
I’m going to give 5 people that retweet this tweet $10,000 each to celebrate Christmas! (Make sure you follow me so I can dm you if you win!). I will show proof!
The only way this season will be consider an asterisk, is if Lebron James and Los Angeles Lakers win. If the Bucks win, people will talk about how great Giannis is and how this may be toughest championship anybody has ever won. The same goes for Kawhi and the Clippers.