https://i.meee.com.tw/094Rnvo.jpg 有票的人 注意罗! The original founders of id Software — John Carmack, John Romero, Adrian Carmac k and Tom Hall — will reunite at QuakeCon 2026. Their appearance has been offic ially announced as part of the convention's 30th-anniversary celebration. The gathering of the original creators has naturally led to speculation about a potential major announcement for one of id Software's classic franchises. Howeve r, they might just be there to celebrate Quake's 30 years with the community. QuakeCon 2026 is scheduled to take place from August 6–9, 2026, returning to th e Gaylord Texan Resort & Convention Center in Grapevine, Texas. Carmack在2026的新想法 AI真的需要dram? https://www.techspot.com/news/111298-john-carmack-proposes-fiber-optic-loops-hig h-speed.html 光的传播需要时间- Carmack把这原本被视为限制的事情, 重新定义成储存资源 Carmack履历: https://i.meee.com.tw/CXUajXO.jpg 彻底影响人类文明的3D图形技术 https://i.meee.com.tw/HiAWt0b.jpg 火箭的垂直起降- 精确飞行控制与物理演算法 https://i.meee.com.tw/BS0rUmg.jpg Oculus VR最关键的低延迟- 非同步时间扭曲ATW技术 https://i.meee.com.tw/PCxHFAz.jpg 未来AGI时代的颠覆性架构思维: 提出光纤环路储存架构, 将光速延迟重新定义为超级数据 缓 冲池 (当然, 这是极有创造力的想法, 但Carmack承认这目前还需要改进, 未来有希望落地) 前去的网友, 别忘了阅读一下Carmack的阅读清单 27篇论文 1. The Annotated Transformer (nlp.seas.harvard.edu) 2. The First Law of Complexodynamics (scottaaronson.blog) 3. The Unreasonable Effectiveness of RNNs (karpathy.github.io) 4. Understanding LSTM Networks (colah.github.io) 5. Recurrent Neural Network Regularization (arxiv.org) 6. Keeping Neural Networks Simple by Minimizing the Description Length of the We ights (cs.toronto.edu) 7. Pointer Networks (arxiv.org) 8.ImageNet Classification with Deep CNNs (proceedings.neurips.cc) 9. Order Matters: Sequence to sequence for sets (arxiv.org) 10. GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelis m (arxiv.org) 11. Deep Residual Learning for Image Recognition (arxiv.org) 12. Multi-Scale Context Aggregation by Dilated Convolutions (arxiv.org) 13. Neural Quantum Chemistry (arxiv.org) 14. Attention Is All You Need (arxiv.org) 15. Neural Machine Translation by Jointly Learning to Align and Translate (arxiv .org) 16. Identity Mappings in Deep Residual Networks (arxiv.org) 17. A Simple NN Module for Relational Reasoning (arxiv.org) 18. Variational Lossy Autoencoder (arxiv.org) 19. Relational RNNs (arxiv.org) 20. Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Au tomaton (arxiv.org) 21. Neural Turing Machines (arxiv.org) 22. Deep Speech 2: End-to-End Speech Recognition in English and Mandarin (arxiv. org) 23. Scaling Laws for Neural LMs (arxiv.org) 24. A Tutorial Introduction to the Minimum Description Length Principle (arxiv.o rg) 25. Machine Super Intelligence Dissertation (vetta.org) 26. PAGE 434 onwards: Komogrov Complexity (lirmm.fr) 27. CS231n Convolutional Neural Networks for Visual Recognition (cs231n.github.i o) (建议先阅读: Attention Is All You Need, Deep Residual Learning, The Unreasonable Effectiveness of RNNs) 以及准备AGI clusters/random access pattern的问题(详见他的X) 八月在Q&A Session提出你的问题! ---- Sent from BePTT on my Google Pixel 10 --

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