← Back to Blog
March 16, 2026 development 1 min read

Anti-Pattern: "Works on My Machine"

Failure pattern #4: No local dev parity. "Works on my machine" delays every sprint. If your local environment doesn't mirror production exactly, you are guessing, not engineering. Docker is non-negotiable.

anti-pattern docker environment parity

The Universal Developer Excuse

Hours wasted on environment mismatches kill velocity and morale.

The Environment Chasm

Mac OS vs Linux production differences break everything. Detailed list of common breakage points.

Enforcing Parity with Docker

docker-compose.yml guarantees identical Postgres, Python, and Node versions for every developer. Full example file and onboarding checklist.

Seamless Transition to Coolify

Same compose file deploys to production PaaS with zero changes. The exact workflow that eliminates "it worked locally" forever.

Enforce Docker on day one for every new hire.

// Related Posts

Mar 16, 2026

The 14-Day Blueprint: Escaping the Endless Sprint Cycle

You don't need another sprint; you need a system. Moving from discovery to production in 14 days isn't about typing faster—it's about a repeatable architecture methodology. No sprints that slip. No handoff chaos. Just a strict transition from Discovery → Design → Deploy.

Mar 16, 2026

PostgreSQL: The Only Database You Actually Need

You don't need MongoDB for documents, Redis for caching, and Pinecone for AI. PostgreSQL does it all. With JSONB columns, pgvector for AI search, and RLS for multi-tenancy, Postgres provides document flexibility without sacrificing relational integrity.

Mar 16, 2026

The Architecture-First Principle: Why Writing Code is Your Last Step

The single biggest mistake development teams make is writing code before the architecture is locked in. Technical debt compounds with every sprint. Refactoring a live system costs 5–10x more than designing it correctly from the start. Here is how to run a 48-hour architecture sprint.

← PreviousEscaping the Zapier Tax: Why I Self-Host n8nNext →Vector Search in Postgres: Preparing Your Data for AI