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How we design and lay out our tables is one of the most important decisions in developing effective and efficient PostgreSQL databases. A database schema is the…

Fast time-series graphs in PostgreSQL, written with Timescale. Using materialized views and continuous aggregates for real-time analytics.

Using PostgreSQL's generate_series function to spin up mock data: integer sequences, date ranges, and time-series data for testing and demos.

Tackling table bloat on large PostgreSQL tables, written with Timescale. Spotting bloat, tuning VACUUM and AutoVacuum, and partitioning with chunks.

Shrinking a PostgreSQL database, written with Timescale. Pages and tuples, how bloat accumulates, routine vacuums, compression, and retention policies.

How adjacency lists and adjacency matrices represent graphs, with tradeoffs and SQL examples for storing a DAG inside a relational database.

Prompt-engineering techniques for LLMs like ChatGPT, Claude, and Bard: persona priming, structured output, few-shot examples.

How to debug Python faster with the built-in breakpoint() and PDB: setting breakpoints, stepping through code, and inspecting variables live.

You don't need Neo4j for graph data. Walking through nodes, edges, and recursive traversals in plain Postgres, with working SQL.

How git worktree lets you check out multiple branches in parallel directories, so hotfixes and feature work never clobber each other again.