If you want to understand Snapdragon X Elite CPU architecture, read chips and cheese substack articles, they're really good.
You will understand ROBs, Cache hierarchy, OOO, n-way decoding, branch predictions etc
A physicist in 1988 got tired of reading bad compiler textbooks.
So Jack W. Crenshaw wrote one himself & put it out for everyone for free about 40 years ago.
It starts with adding just two numbers & by part 15 you have a basic compiler up & running.
two professors at Wisconsin spent 25 years teaching operating systems together
then they wrote a 714 page textbook about "Operating Systems: Three Easy Pieces"
it covers virtualizing the CPU virtualizing memory concurrency persistence security and file systems
small enough to read in parts and also it is written like a conversation not a typical textbook
this is what you read if you want to really understand how operating systems work not just the theory
I dreamt of writing a book for as long as I can remember, wrote many outlines, but none materialised.
Then I started writing this article on virtual memory, which kept getting longer and longer and turned into an unplanned book.
Based on all the positive feedback, I recently published it on Amazon as well. Somehow, it is showing up as the #1 bestseller in its category on Amazon India :D.
Maybe it says something about how niche these categories are, but it still feels surreal.
This paper deserves more attention - A Wake-Up Call for Kernel-Bypass on Modern Hardware
Oracle Exadata already proves this in production - cutting data access latency by 17x in a real database system.
The paper makes the case for why this needs to become the norm, not the exception.
Link to paper:
https://t.co/NTlgSFXJW8
Oracle Exadata
https://t.co/ZZ4R4aQUZV
Stop learning LLM internals from random one-off tutorials.
LLM Internals is a step-by-step GitHub learning repo for understanding how large language models work under the hood.
It helps you build a cleaner mental model by organizing blogs and videos from tokenization to attention math, Transformer components, training concepts, and inference optimization.
Key features:
• Fundamentals first – starts with LLMs, RAG, MCP, agents, fine-tuning, quantization, tokenization, and BPE
• Attention math – walks through Q/K/V, √dₖ scaling, causal masking, RoPE, and grouped-query attention
• Transformer components – covers the architecture, feed-forward networks, normalization, MoE, and LoRA
• Training concepts – includes backpropagation, cross-entropy loss, RLHF, and reasoning models
• Inference optimization – covers KV cache, paged attention, Flash Attention, speculative decoding, continuous batching, and prompt caching
It’s open-source (Apache License 2.0).
Link in the reply 👇
@TrisH0x2A me, and the majority of the OSDev community, recommend to not write your own bootloader and actually something like limine and its template https://t.co/lEgL5wrrDA