This blog is broad enough that jumping in randomly is often the wrong way to read it.
The easier way is to pick a direction first:
- stronger systems intuition
- ML systems and large-model infrastructure
- language and compiler foundations
- developer platform and production engineering
If You Want Stronger Systems Intuition
Start with:
Why this path:
These series make the rest of the blog easier to read. They build the kind of mental model that helps with performance, runtime behavior, memory, scheduling, and lower-level debugging.
Suggested order:
If You Want To Study GPU / LLM Infrastructure
Start with:
Why this path:
These three series are meant to fit together. GPU Systems explains how the hardware and kernels behave. PyTorch Internals explains how those kernels meet real training code. Distributed LLM Training explains what happens once the training system spreads across many GPUs and nodes.
Suggested order:
If You Care About ML In Production
Start with:
Why this path:
These series are less about model math and more about operating real systems: pipelines, deployment, observability, self-service, platform constraints, and production tradeoffs.
Suggested order:
- MLOps
- Platform Engineering
- Distributed LLM Training if you want to move toward large-scale training systems
If You Want Language / Compiler Foundations
Start with:
Why this path:
Compiler work becomes much easier to understand when you can connect formal language ideas to runtime and hardware behavior instead of treating them as separate textbooks.
If You Prefer A Gentler Entry Point
Start with:
These two are easier entry points than the heavier systems tracks, while still connecting well to the deeper material later.
A Good General Reading Order
If you want one reasonable long-term path through the blog:
- Computer Architecture
- Linux
- Compiler
- GPU Systems
- PyTorch Internals
- Distributed LLM Training
- MLOps
- Platform Engineering
That is not the only order, but it is a coherent one.