The First PrinciplesPath
Master computing from the ground up — 13 domains integrating theory and practice, from digital logic through infrastructure at scale and the formal limits of computation.
13 sections
Getting Started
Career landscape, environment setup, learning strategy, and the integrated assessment model.
How Computers Work
Digital logic, Boolean algebra, processor architecture, memory hierarchy, data representation, and the boot process.
Operating Systems and Linux
Processes, scheduling, memory management, file systems, permissions, namespaces, cgroups, and system administration — theory and practice unified.
Text Processing and Automation
Vim, shell scripting, regular expressions, text processing pipelines, and task scheduling — the tools that turn manual operations into repeatable, reliable automation.
Programming Fundamentals
Python, OOP, functional programming, testing, error handling, and project structure.
Data Structures and Algorithms
Formal data structures, complexity analysis, algorithm design paradigms — implemented and profiled.
Networking
Network models, protocols, routing, DNS, HTTP, TLS, SSH, firewalls, and packet-level troubleshooting.
Data Management
Relational model, SQL, normalization, transactions, indexing, NoSQL, and database operations.
Security and Cryptography
CIA triad, encryption math, PKI, authentication, OWASP, hardening, secrets management, and compliance.
APIs and Integration
REST, authentication, data formats, client resilience, GraphQL, gRPC, and webhooks.
Software Engineering and Collaboration
Git internals, design patterns, architecture, CI/CD, deployment strategies, and GitOps.
Infrastructure at Scale
Containers at the kernel level, Kubernetes architecture and operations, and Terraform with state management.
Theory of Computation
Automata, computability, complexity theory, and the formal limits of what machines can do.