Systems thinking predates writing itself — humans have always reasoned about wholes, not just parts. This book traces that lineage from cybernetics and Forrester's world models to modern complexity science, digital twins, and AI-assisted simulation. What does it mean to think in systems in 2026, and why does it matter more than ever?

A practical guide to question-driven thinking — how to find the questions worth asking, why most people never do, and how to develop the habit of asking what nobody else thought to ask.

HPC for the modern era — GPU computing, CUDA vs ROCm vs Metal, MPI, OpenMP, vectorization, and the intersection with ML workloads. Practical for engineers, not just academics.

A developer's guide to cryptography in the post-quantum era — covering lattice-based algorithms, NIST standards like CRYSTALS-Kyber and Dilithium, and what "harvest now, decrypt later" means for systems you're building today.

kdb+/q is powerful but lives in a silo. This book tears down the walls — integrating Q with Rust, Python, and R, finding the IDE plugins that actually work, and connecting kdb+/q to web frameworks and mainstream tooling. For the Q developer who refuses to be an island.

Reinforcement Learning concepts from Sutton & Barto's canonical text, implemented in kdb+/q. Bandits, dynamic programming, TD learning, and Q-learning — all in the language built for high-performance data. The book Dmitriy wished existed when he started.

CAP theorem, eventual consistency, replication, and fault tolerance — explained for engineers who aren't at Google scale but need to think like they are. No hand-waving, no oversimplification. Just the mental models that make distributed systems decisions less terrifying.

Rust on the server

Roll your own

OSINT in 2026