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Synthetic vs Real Data
Synthetic data vs real data has no single winner. The answer flips between statistical work and application databases. Name the job first.
Jul 24, 2026Read article →Training Data for AI Agents
Training data for AI agents that run software isn't labeled datasets. It's a running environment, and its floor is a populated, connected database.
Jul 24, 2026Read article →Agents & Empty Databases
An AI agent empty database makes tests pass and demos render while the tables sit bare. Seed connected, realistic rows so green means something.
Jul 18, 2026Read article →Demo Data Generator
A demo data generator that fills flat rows breaks once your tables relate. Generate realistic, schema-consistent demo data from your live schema instead.
Jul 18, 2026Read article →MCP Test Data
MCP test data, surveyed: which MCP servers generate realistic, relational rows for your database, and which only query data that already exists.
Jul 18, 2026Read article →Pytest Database Fixtures
Pytest database fixtures done right — real scope semantics, the SQLAlchemy SAVEPOINT rollback pattern, and how to seed a Postgres test database once.
Jul 18, 2026Read article →Vibe Coding the Database
Vibe coding database guide: the schema and migrations mostly work, the data is where it breaks. How to handle the database when you vibe-code an app.
Jul 18, 2026Read article →Agentic QA & Test Data
Agentic QA test data is the gap the testing agents don't fill: they write and run the tests but can't invent the realistic, connected rows underneath.
Jul 15, 2026Read article →GitHub Actions Postgres Seeding
GitHub Actions seed Postgres database, done right: service container, pg_isready health gate, migrate-then-seed order, and three ways to fill it.
Jul 15, 2026Read article →