Why are your models so big? (2023)(pawa.lt)
23 points by jxmorris12 | 13 comments
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TinyML: Ultra-low power machine learning(ikkaro.net)
349 points by Gedxx | 84 comments
An Ikkaro blog article explaining TinyML, covering model compression techniques like pruning, quantization and distillation, tools, and uses on low-power devices.
Tools for Learning LLVM TableGen(blog.llvm.org)
141 points by matt_d | 16 comments
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Something weird is happening with LLMs and chess(dynomight.substack.com)
626 points by crescit_eundo | 424 comments
A Dynomight newsletter post examining how well large language models play chess and why results vary strangely across models.
LLM Visualization(bbycroft.net)
568 points by gmays | 39 comments
An interactive 3D visualization and walkthrough of how a GPT large language model works, starting with a tiny nano-gpt model that sorts letters.
AI Made Writing Code Easier. It Made Being an Engineer Harder(ivanturkovic.com)
343 points by saikatsg | 242 comments
Blog post by Ivan Turkovic arguing that AI tools make producing code easier while making the broader job of being a software engineer harder.
That's not an abstraction, that's a layer of indirection(fhur.me)
546 points by fagnerbrack | 213 comments
A blog post arguing that many so-called abstractions in codebases are merely layers of indirection that add complexity and hurt performance without real value.
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.
AI has access to a vastly larger working memory than the human brain(davidepiffer.com)
524 points by rzk | 452 comments
A Substack essay by Davide Piffer arguing that AI's edge over mathematicians comes from a vast symbolic working memory rather than superior reasoning.