Kubernetes Foundations
A practical book on running and configuring workloads on Kubernetes—from the Linux kernel up through Pods and Services—with clear diagrams and worked examples.
Learning Resources
Three shelves. Each tile is a book or a guide, and every one opens to the same numbered index — so a section is always one click away.
01
Running workloads for real: Linux foundations, Kubernetes, storage, and platform operations.
1 live · 1 on the shelfHands-on tools
02
From classic ML and neural networks to transformer architectures, RAG, and agentic systems.
5 live · 5 on the shelfHands-on tools
Hand-inked digital notes for the models that matter — one page per shape, blue for explanation, red for what to remember. Built to grow into a shelf of a hundred.
Enhance Retrieval-Augmented Generation with agency: query analysis, rewriting, multi-step retrieval, and self-evaluation.
The basics of machine learning: learning paradigms, common algorithms, and the end-to-end workflow.
The architecture and training of neural networks, from neurons and activations to backpropagation.
Building real-world natural language processing solutions with modern transformer models.
03
The broad engineering base everything else sits on: networking, security, and the algorithms underneath.
0 live · 2 on the shelfHands-on tools
Threat modelling, authentication vs authorization, applied cryptography, OWASP basics, and software supply-chain hygiene.
How bytes actually move: the TCP/IP stack, DNS, HTTP and TLS, load balancing, and debugging a request end to end.