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Deep Learning Is Applied Topology(theahura.substack.com)
473 points by theahura | 176 comments
A Substack essay from 12 Grams of Carbon arguing that deep learning is applied topology, explaining how neural networks transform data lying on manifolds.
Deep Learning Is Not So Mysterious or Different(arxiv.org)
445 points by wuubuu | 112 comments
arXiv paper by Andrew Gordon Wilson arguing deep learning generalization phenomena like benign overfitting and double descent are explainable by soft inductive biases.
The new skill in AI is not prompting, it's context engineering(philschmid.de)
832 points by robotswantdata | 467 comments
Blog post by Philipp Schmid arguing that context engineering, supplying LLMs with the right information and tools, matters more than prompting for AI agents.
Deep learning gets the glory, deep fact checking gets ignored(rachel.fast.ai)
549 points by chmaynard | 134 comments
Rachel Thomas blog post on how a high-profile Nature paper using a Transformer to predict enzyme functions contained many errors that received far less attention than the original result.
It's Always TCP_NODELAY(brooker.co.za)
446 points by eieio | 160 comments
Marc Brooker's blog post arguing that Nagle's algorithm is outdated and TCP_NODELAY should be enabled to avoid latency issues in distributed systems.
Thinking fast and slow in AI: The role of metacognition (2021)(arxiv.org)
165 points by teleforce | 68 comments
An arXiv paper proposing a multi-agent AI architecture inspired by Kahneman's fast and slow thinking, with a metacognitive module choosing between system 1 and system 2 agents.












