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 (科学空间 / kexue.fm) is a science blog called Scientific Spaces that publishes posts on a wide range of natural science topics, especially math, astronomy, physics, machine learning, and optimization methods like AdamW, MuP, and diffusion models. It mixes research-level explanations with tutorials and math derivations aimed at practitioners and enthusiasts. Scientific Spaces

Several links to educational or resource pages: the MIT lecture PDF (6.189 Lecture on parallel design patterns for concurrent programming), a personal math site (Justin Skycak’s offerings including an Introduction to Algorithms and Machine Learning book/tutorial), and a page on Exploring Mathematics with Python from PSU. These focus on core computer science concepts and applied math instruction. The JustinMath site also includes reflections on advanced math/CS education outcomes (the Eurisko Sequence). groups.csail.mit.edu+2coe.psu.ac.th+2

The social snippet (X post) is praise of the experience of working with well-known open-source and systems programmers (like Ted Ts’o and Eric Biggers), highlighting admiration for highly productive coding skill. The arXiv paper 1703.09039 referenced is a foundational survey on efficient processing of deep neural networks (deep learning hardware/software tradeoffs), widely cited in embedded/efficient AI contexts. csc.ucdavis.edu+1

Finally, the Notion Transformer link discusses the Transformer neural network architecture — the deep learning model behind attention mechanisms in modern language and vision models (e.g., “Attention Is All You Need”). The JustinMath PDF and other resources complement this by offering algorithmic and machine learning foundations.

https://www.notion.so/Transformer-c0a6ab89e04543ffa855170cef2759fa

https://groups.csail.mit.edu/cag/ps3/lectures/6.189-lecture6-patterns1.pdf

https://naklecha.com/about

https://coe.psu.ac.th/ad/explore/

https://x.com/ciphergoth/status/2006446942453387675 "I've worked with some incredible people - you might have heard of Jaegeuk Kim or Ted Ts'o - and some ridiculously productive programmers - Eric Biggers, Jeff Sharkey and

@jackinwarsaw

come to mind as people who seemed to solve problems with code at a truly unearthly rate."

https://www.justinmath.com/books/#introduction-to-algorithms-and-machine-learning

https://www.justinmath.com/files/introduction-to-algorithms-and-machine-learning.pdf

https://www.justinmath.com/math-academys-eurisko-sequence-5-years-later/

https://arxiv.org/pdf/1703.09039