Reading path#
The collection order is intentional. Cloud infrastructure starts with the boundaries around a production application; low-level infrastructure then opens the Linux, hardware, container, and virtual-machine layers hidden beneath those services. Distributed systems adds independent clocks, partial failure, replication, and coordination, and data systems applies those mechanisms to storage engines and databases.
System design combines the earlier mechanisms into an interview and production-design method. AI inference infrastructure specializes that method for model serving and GPUs, while harness engineering starts at the model boundary and builds the runtime that gives an agent context, tools, durable state, verification, and operating controls. A reader can start with one specialty; each collection index names the background needed and points to slower explanations when a lower layer matters.
Collections
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Cloud infrastructure
AWS compute, networking, identity, storage, Kubernetes control systems, queues, deployments, reliability, and recovery.
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Low-level infrastructure
Linux boundaries, scheduling, memory, I/O, networking, containers, debugging, virtual machines, and assigned devices.
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Distributed systems
Failure, time, coordination, replication, partitioning, distributed storage, dataflow, and recovery from first principles.
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Data systems
Storage engines, indexes, transactions, relational databases, document stores, key-value systems, columnar files and table formats, search engines, and change data capture.
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System design
Requirements, capacity, request paths, data systems, coordination, asynchronous work, reliability, and design cases.
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AI inference infrastructure
Build an inference service from the first token and tensor through GPU execution, distributed serving, measurement, and safe rollout.
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Harness engineering
Agent contracts, context, MCP and tools, durable state, verification, safety, orchestration, evaluation, and maintenance.
