Engineers and engineering leaders who build, deploy, and scale software systems.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Securing software systems — application security, threat modeling, authentication, and infrastructure protection.

Designing scalable, reliable software systems — architecture patterns, distributed systems, and technical tradeoffs.

Measuring and evaluating AI model performance, quality, and reliability for production use cases.

Designing and optimizing the context window for LLM applications — prompt structure, memory, and information retrieval.

Operating and deploying large language models in production — fine-tuning, inference, monitoring, and cost optimization.

Model Context Protocol — building tool integrations and server connections for AI agents.

Learn when to build a workflow, an agent or a team of agents by shipping two production systems, each measured by the outcome it delivers.

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