You ask your coding agent to find every caller of a function before you touch it. Ask again tomorrow, same repo, same question. It starts from zero and does the whole crawl again.
[Karpathy’s] argument: stop treating LLMs as search engines that re-read the same documents on every question. Compile the knowledge into a structure once. Query the structure instead. His point was never about wikis. It was about not making an LLM redo work it already did. Read More
Daily Archives: August 2, 2026
Learning Depends on Refining Existing Neural Connections
A study demonstrates that learning in neural networks is driven primarily by adjusting the strength of existing connections rather than by continuously expanding or reconfiguring underlying network architecture.
Published in Physica A, the study by Prof. Ido Kanter of Bar-Ilan University’s Department of Physics and the Gonda (Goldschmied) Multidisciplinary Brain Research Center explored this longstanding question using artificial neural networks trained on language-learning tasks.
As the amount of training data increased, the models became significantly better at learning. Surprisingly, however, the researchers found that the networks could still lose roughly the same proportion of connections (synapses) without any meaningful decline in performance. In other words, improved learning did not depend on building more complex networks. Instead, it resulted from more effective cooperation among the components that were already there. — Read More
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