AI Engineer's Guide to Data Engineering
Feature stores, data pipelines, streaming, and batch processing for AI systems
Feature stores, data pipelines, streaming, and batch processing for AI systems
Essential machine learning theory, neural network architectures, and training fundamentals every AI engineer must know
Knowledge graphs, Neo4j, RDF, and ontology engineering for AI applications
From BM25 to RAG: understanding search systems that power modern AI applications
Deep dive into vector databases, embeddings, and similarity search for production AI systems
Master the /compact command to manage context and save tokens in long sessions.
Why starting fresh sessions often produces better results than continuing long ones.
How to use Claude Code for commits, branches, PRs, and code review.
Set up hooks to auto-format, lint, test, and validate code after Claude makes changes.
How to set up MCP servers to connect Claude Code with databases, APIs, and other tools.
How to choose between Haiku, Sonnet, and Opus for different tasks.
Use bash scripts to reduce Claude Code token usage and automate repetitive tasks.
Why skills reduce token usage and how to leverage on-demand loading.
Build project-specific commands to speed up repetitive workflows.
How to use subagents for faster codebase exploration and parallel task execution.
How Claude Code token usage works and how to estimate your costs.
16 posts with tag "intermediate"