{"slug": "show-hn-ilya-s-30-papers-explained-in-audio", "title": "Show HN: Ilya's 30 papers, explained in audio", "summary": "A new project turns Ilya Sutskever's famous list of 30 papers for John Carmack into AI-narrated audio episodes, explaining key insights from foundational deep learning works. The list, which Sutskever said covers 90% of what matters in modern AI, includes papers on transformers, scaling laws, and information theory.", "body_md": "THE AI READING LIST\n\n# Ilya’s 30 papers as audio\n\nThe famous list of papers Ilya Sutskever gave John Carmack. He said, “If you really learn all of these, you’ll know 90% of what matters today”.\n\nThe audio episodes explain the key insights, giving you a clear overview of every chapter before reading the full PDFs.\n\n## Listen to the episodes\n\nAI-narrated, key-insights chapter by chapter. Free to stream and download.\n\n## Also on the list\n\nCourses, books and code-heavy pieces that don’t fit an audio episode — read them at the source.\n\n- Article\n### The Annotated Transformer\n\nSasha Rush et al. (Harvard NLP) · 2018\n\n- Book\n### Kolmogorov Complexity and Algorithmic Randomness\n\nA. Shen, V. Uspensky, N. Vereshchagin · 2017\n\n- Course\n### CS231n: Convolutional Neural Networks for Visual Recognition\n\nAndrej Karpathy, Fei-Fei Li et al. (Stanford) · 2016\n\n- Book\n### Machine Super Intelligence\n\nShane Legg · 2008\n\n- Paper\n### A Tutorial Introduction to the Minimum Description Length Principle\n\nPeter Grünwald · 2004\n\n## THE STORY\n\nAs the story goes, when legendary game programmer **John Carmack** (Doom, Quake) decided to move into AI, he asked OpenAI co-founder **Ilya Sutskever** what he should read. Ilya handed him a list of around thirty papers and said that if Carmack really learned all of them, he’d understand **90% of what matters** in modern deep learning.\n\nCarmack has confirmed the exchange in interviews, though he’s said the original list was lost. The version circulated today was reconstructed by the community from his and others’ recollections. It’s a remarkably coherent tour from convolutional and recurrent nets, through attention and Transformers, to scaling laws and the information-theoretic roots of learning.\n\nReconstructions and background: [community reading list](https://github.com/dzyim/ilya-sutskever-recommended-reading), [Aman’s AI Journal](https://aman.ai/primers/ai/top-30-papers/), [Ilya’s List](https://gonzoml.substack.com/p/deep-learning-legends-ilyas-list).\n\n## Have a paper of your own?\n\nUpload any PDF or paste a link and ListenDock turns it into a clear audio episode.\n\n[Turn a paper into audio](/pdf-to-mp3)", "url": "https://wpnews.pro/news/show-hn-ilya-s-30-papers-explained-in-audio", "canonical_source": "https://listendock.com/30-papers", "published_at": "2026-07-09 22:10:03+00:00", "updated_at": "2026-07-09 22:36:39.644697+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-products", "ai-tools"], "entities": ["Ilya Sutskever", "John Carmack", "OpenAI", "ListenDock", "Sasha Rush", "Andrej Karpathy", "Fei-Fei Li", "Shane Legg"], "alternates": {"html": "https://wpnews.pro/news/show-hn-ilya-s-30-papers-explained-in-audio", "markdown": "https://wpnews.pro/news/show-hn-ilya-s-30-papers-explained-in-audio.md", "text": "https://wpnews.pro/news/show-hn-ilya-s-30-papers-explained-in-audio.txt", "jsonld": "https://wpnews.pro/news/show-hn-ilya-s-30-papers-explained-in-audio.jsonld"}}