Software engineering curriculum

How to use this curriculum

Start with a track when you need a domain map, choose a roadmap when you want an ordered path, and open a concept when you need a focused mental model. Every concept connects reading to active work through a drill, an explain-back review prompt, and a build artifact. The interactive app adds FSRS spaced repetition, Monaco code execution, Excalidraw system diagrams, and Socratic feedback.

The public pages are useful without an account or JavaScript. They deliberately expose learning structure and primary sources, while private progress, notes, saved reading, and review answers remain inside the personal learning workspace.

Browse all tracks

Choose a sequenced roadmap

Requested domain coverage

The expanded taxonomy maps 11 broad domains and 96 named subtopics to stable concepts. This is a breadth and navigation contract: depth comes from completing the linked drills, roadmaps, and artifacts.

• Systems Foundations: Operating systems, Networking, Concurrency and parallelism, Memory, CPU, GPU and storage, Runtime and performance engineering, Security and isolation
• Infrastructure & Platforms: Cloud infrastructure, Containers and Kubernetes, CI/CD and developer environments, Scheduling and orchestration, Reliability and fault tolerance, Observability and OpenTelemetry, Sandboxes and execution environments, Infrastructure automation
• Distributed Systems: Consensus and coordination, Replication and partitioning, Messaging, Kafka and event systems, Caching, Distributed workflows and Temporal, Consistency models, Resilience and failure recovery
• Databases & Data Systems: Storage engines, Transactional databases, Analytical databases, Distributed databases, Streaming systems, Search and vector databases, Indexing and query execution, Data warehouses and lakehouses, Memory-versus-disk architecture
• AI Models & Training: Model architectures, Transformers and tokenization, Pre-training, Fine-tuning, Post-training, Reinforcement learning, Quantization, Open-weight models, Multimodal models
• Inference & Serving: vLLM and inference engines, Continuous batching, KV caching and PagedAttention, FlashAttention, Speculative decoding, Model routing, GPU utilization, Cost and latency optimization, Local and on-device inference, Inference hardware
• Agent Systems: Agent loops and harnesses, Tool use, Memory and context management, MCP and integrations, Multi-agent coordination, Durable execution, Permissions and sandboxing, Browser and computer-use agents, Agent communication and interfaces, Long-running and scheduled agents
• Evaluation, Verification & AI Reliability: LLM evaluations, Coding-agent benchmarks, Tool-use evaluations, Regression testing, Hallucination and failure detection, Agent observability, Tracing and replay, Evidence-backed verification, Human review systems, Cost, latency and quality measurement
• Developer Tools & Code Intelligence: Code review, Static and dynamic analysis, Testing infrastructure, Codebase graphs, Dependency and blast-radius analysis, IDE and CLI tooling, Coding agents, Repository intelligence, Software supply-chain health, Automated debugging and remediation
• Product & Application Engineering: Backend and API architecture, Web and mobile engineering, Product analytics, UX and interface design, Real-time applications, Voice interfaces, Computer vision, 2D/3D interactive systems, Product distribution and growth loops
• Multimodal, Robotics & Spatial Computing: Vision models, Pose and motion tracking, Voice and audio systems, Image and video generation, On-device intelligence, Robotics, Spatial interfaces, Human-computer interaction