Systems

Deep Learning Systems (CMU 10-414/714)

10-414/714: Deep Learning Systems at CMU — a publicly available course on building the components of a deep learning framework from scratch, including automatic differentiation, optimization, and hardware acceleration. One of the best resources for understanding how frameworks like PyTorch actually work.

CodeCrafters: Advanced Programming Challenges

CodeCrafters offers advanced programming challenges where you rebuild real systems — Redis, Git, SQLite, a Unix shell — from scratch in your own IDE using Git-push-based testing. Designed for experienced developers who want systems-level depth.

Eli Bendersky's Website

Eli Bendersky's personal blog is a deep-dive technical reference for systems programming, compilers, and Go/Python internals. His posts on how things work at the implementation level — parsers, ELF binaries, coroutines, LLVM — are consistently among the best on the internet.

Formal Verification of an OS Kernel

Transformer Inference Arithmetic

Carol Chen (kipply) works through the arithmetic of transformer inference — compute vs. memory bandwidth, KV cache sizing, and how batch size shapes throughput/latency tradeoffs. Essential reference for anyone reasoning about LLM serving costs.

Algorithms for Modern Hardware

A free online textbook on algorithms for modern hardware — covers SIMD, cache optimization, branch prediction, and CPU microarchitecture from a performance engineering perspective. One of the most practical resources for writing truly fast code on real hardware.

Stanford CS 329S — Machine Learning Systems Design

Stanford CS 329S Machine Learning Systems Design — Chip Huyen's course on building production ML systems. Covers the full lifecycle from problem framing through data, training, deployment, and monitoring with real-world case studies.

CMU 15-721: Advanced Database Systems

CMU 15-721 is Andy Pavlo's advanced database systems course covering in-memory databases, query compilation, concurrency control, and storage engines — the internals that most engineers never see. Free lectures, reading list of seminal papers, and a reputation as one of the best systems courses available.

Action Item: How to Become a Machine

An Every/Superorganizers piece on building reliable personal systems for consistent output — treating productivity as an engineering problem of removing variance rather than maximizing peak performance.

Powering Search and Recommendations at DoorDash

DoorDash's engineering blog post on their search and recommendation systems — covering how they rank restaurants and dishes, handle cold-start problems, and personalize results. A practical look at production recommendation systems at a major food delivery platform.

Virtualization Performance: Zones, KVM, Xen

Brendan Gregg's benchmark comparing the performance overhead of Solaris Zones, KVM, and Xen virtualization at Joyent. A rare empirical comparison from someone with the systems instrumentation expertise (DTrace) to measure what actually matters.