Tutorials

Configure PgBouncer in transaction mode in front of your PostgreSQL database so connection limits are not hit when Django scales beyond a handful of workers.

Replace local media file storage with Cloudflare R2 using boto3 so uploaded files persist across deployments and are served from a CDN edge.

Structure Django settings and Render service configuration so secrets, database URLs, and environment-specific values are managed cleanly from day one.

Configure Celery Beat health checks, Flower monitoring, and failure alerting so you know when a background worker has stopped processing before it causes a user-facing problem.

Use Certbot and Nginx to provision and auto-renew free TLS certificates so your site is HTTPS-only with no manual certificate management.

Set up a production-ready Docker Compose environment for Django, PostgreSQL, Redis, and Celery while avoiding the rebuild, port, and database readiness mistakes that waste hours of debugging.

Build an AI classification pipeline that flags submitted content for human review, reducing moderator workload without removing human oversight.

Combine vector similarity search with keyword filtering to build a search experience that finds relevant results even when exact words do not match.

Build a lightweight evaluation loop that scores AI responses against a golden dataset so you can detect regressions before users notice them.

Track monthly token usage per user in your database and enforce soft and hard limits so your AI feature scales without runaway API costs.

Use the OpenAI or Claude tools parameter to guarantee structured JSON output so you can parse AI responses in code without fragile string matching.

Implement exponential backoff, per-task retry logic, and user-facing error states so your AI features degrade gracefully instead of silently failing.