Programming Tutorials

  In 2025, the debate between FastAPI and Django is no longer just about "speed vs. batteries-included." It has shifted entirely toward concurrency models required by Generative AI. If you are building an MVP with standard CRUD (Create, Read, Update, Delete) requirements, Django remains the productivity king. However, if you are building AI Agents that require streaming LLM tokens, handling WebSocket connections for real-time reasoning, or managing high-throughput microservices, the synchronous history of Django becomes a bottleneck. This guide dissects the architectural differences, analyzes the root causes of performance divergence, and provides the exact code patterns needed to build AI-native backends today. The Core Conflict: Thread Blocking vs. Event Loops The friction between these frameworks stems from how they handle I/O-bound operations—specifically, the latency introduced by calling external Large Language Models (LLMs) like GPT-4 or Claude. The Django Bottleneck (...

  The upgrade from Pydantic V1 to V2 is not merely a version bump; it is a paradigm shift. With the core logic rewritten in Rust ( pydantic-core ), V2 offers significant performance gains but introduces strict validation rules that break legacy V1 implementations. Backend engineers migrating FastAPI applications or data pipelines often encounter two specific blockers: deprecated validator signatures referencing  FieldValidationInfo  (or  values  dictionaries) and runtime validation errors claiming "Input should be a valid dictionary." This guide provides the root cause analysis and production-ready code required to migrate complex validation logic to the Pydantic V2 standard. The Root Cause: Rust Core and Validator Topology In Pydantic V1, validation was pure Python. It was permissive regarding input types and allowed accessing sibling fields via a loosely typed  values  dictionary in validators. Pydantic V2 moves validation to Rust. This results in: S...