Long-form posts on data engineering and ML in production.
Substantive technical writing in the spirit of an engineering journal — not marketing posts, not changelogs.
The Stack Evolution
Six packs from sync baseline to specialized infra — each one triggered by a specific pain point, never by chasing newness. Django + Next.js on a real stack progression.
#architecture#django#next.js#asgi#celery#websocketsFor operations that already exist.
Why Oriented Platforms is for businesses that already have data, infrastructure, and stakes — not idea-stage MVPs.
#positioning#engineeringML in production: the gap between a trained model and a thing customers depend on.
Training a good model is one thing; running it as a system real customers depend on is another, and the gap is mostly the unglamorous engineering that keeps it shipped — idempotency, queue discipline, observability, schema evolution, and deploys that don't drop users.
#ml#production#engineeringConnect → clean → model: data orchestration patterns we ship with.
Idempotent handlers, at-least-once delivery, dead-letter routing, back-pressure, schema evolution, and pipeline observability — the recurring patterns that show up in every data pipeline we ship into production. The orchestrator is downstream of these.
#data-orchestration#production#engineering