Ship a real webhook delivery service with idempotency, retries, and a dead-letter queue — a portfolio project that signals genuine distributed-systems skill to hiring managers.
How to design, scale, and debug Celery pipelines in production — covering broker choice, result backends, canvas workflows, and the failure modes that hit hardest.
Demystify how LLM inference works by building a tiny inference engine from scratch. Covers tokenization, the transformer forward pass, KV cache, and decoding.
How to build FastAPI services that survive contact with GPU workloads, long inference calls, and real users. Patterns, anti-patterns, and production code.
A deep‑dive into Celery’s architecture, scaling tactics, and observability practices that let engineers ship reliable distributed Python workloads.