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THE DAILY INDEX

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ARTICLES & TUTORIALS

TLDR Dev

AUTO-RESEARCH WITH CODEX: HOW I ACHIEVED A 232X FASTER KERNEL OVER BASELINE WITH CODEX IN GPU MODE'S QR_V2 PROBLEM

In a recent auto-research contest focusing on GPU optimizations, an impressive 232x speedup over the baseline was achieved in implementing the batched square compact-Householder QR factorization problem, with the author finishing 12th out of 183 participants. The project involved using techniques like the blocked Householder algorithm, continual kernel profiling, and iteration on ideas presented through advanced AI models.

MODELS ARE GETTING DUMBER ON PURPOSE

Recent advancements in AI models show that while their reasoning capabilities continue to improve per parameter, their factual recall has notably declined, suggesting a deliberate trade-off where knowledge is sacrificed for reasoning skills. This shift allows models to operate effectively by relying on external information sources to provide up-to-date facts, reducing issues like hallucination by enabling the model to reference and verify information against live data rather than fixed weights.

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