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Uses generated examples to test stable invariants while preserving readable examples and reproducible failures.
Explains per-thread state, pooled-thread leakage, context cleanup, hidden dependencies, and virtual-thread scaling concerns.
Separates reactive synchronization inputs from non-reactive logic that still needs the latest props or state.
Evaluates whether read-heavy access justifies a reader/writer lock instead of a simpler lock or immutable snapshot.
Chooses a Django user model early and preserves swappable references without turning it into a profile dumping ground.
Varies DRF query and representation behavior deliberately across list, detail, and write actions.
Chooses between FastAPI-managed response processing and direct control over the HTTP response.
Chooses between synchronous and asynchronous FastAPI handlers based on the libraries and work they execute.
Prefers typed APIs for reliable actions and reserves UI automation for constrained cases with stronger observation, approval, and recovery.
Chooses request-response, streaming, or durable background execution based on duration, user interaction, reliability, and side effects.
Separates missing resources, canonical navigation, authorization outcomes, expected failures, and unexpected exceptions.
Separates source state, derived state, and synchronization with external systems to prevent reactive loops.
Uses fixture factories and test-data builders to keep setup flexible, readable, and owned by each test.
Exposes a small focus, scroll, or media API while keeping component internals private and preferring declarative props.
Aligns selected database data with an API read model and avoids unnecessary ORM state.
Connects static ownership to constants and stateless operations while exposing the risks of global mutable state and initialization.
Uses pre-filtering for authorization and hard constraints while recognizing engine-specific recall and performance behavior.
Uses task-aware context for tracing while keeping business inputs explicit and correctly reset.
Uses cryptographically secure token generation and timing-resistant comparison at credential boundaries.
Rejects agent complexity when rules, APIs, search, ordinary automation, or a bounded model call solve the task more safely.
Identifies tasks better served by deterministic code, search, rules, or conventional models because of correctness, latency, cost, or control.
Overrides unstable external boundaries intentionally while keeping the framework behavior and real collaborators the test claims to prove.
Balances one-call simplicity against explicit multi-step workflows with intermediate validation and failure handling.
Uses Django signals selectively and makes transaction timing and side effects explicit.