Game Development
GUT (Godot Unit Test) is the de facto unit testing framework for Godot games. It integrates directly into the Godot editor, lets you write tests in GDScript alongside your game code, and runs headlessly for CI pipelines. This guide covers installation, writing your first tests, and the assertion API. Key
Testing
SendGrid provides a sandbox mode that validates emails without sending them, dynamic templates you can test independently, and event webhooks for tracking delivery status. This guide covers testing SendGrid integrations at every level: sandbox validation, template rendering, webhook handling, and deliverability simulation. Email bugs are subtle and slow to surface.
Testing
Twilio provides test credentials that accept API calls without sending real messages, specific magic phone numbers for simulating delivery failures, and a webhook testing workflow for voice and SMS. This guide covers unit testing Twilio integrations with mocks, integration testing with test credentials, and testing TwiML responses. Twilio integrations fail
Testing
Go's testing package has no built-in snapshot testing, but two libraries fill the gap: cupaloy (simpler, widely used) and go-snaps (more features, testify integration). Both capture test output to files, compare on future runs, and update via an environment variable flag. This guide covers both with practical examples.
Testing
Stripe provides a test mode with real API behavior, test card numbers for different scenarios, and a CLI for webhook testing. Testing a payment integration requires covering happy-path charges, declined cards, 3D Secure flows, webhook receipt and idempotency, refunds, and subscription lifecycle events. This guide covers all of them. Payment
Testing
Basic MCP server tests cover tool invocation and resource reading. Advanced patterns go further: property-based testing to discover unexpected inputs, fuzz testing for protocol robustness, multi-client concurrency testing, transport-layer validation, and performance benchmarking. This guide covers those advanced patterns. A previous guide covered the basics of testing MCP servers — unit
Testing
Syrupy is the most popular snapshot testing plugin for pytest. It stores snapshots in __snapshots__/ directories alongside your test files, uses YAML-like .ambr format for human-readable diffs, and integrates with pytest's assertion rewriting for clear failure messages. This guide covers installation, writing snapshot tests, updating snapshots, and handling
Testing
AI agent pipelines need CI/CD just like any software — but standard pipelines don't account for LLM non-determinism, evaluation costs, or model-level regressions. This guide covers building CI/CD pipelines specifically for AI agent systems: fast deterministic checks, LLM evaluation gates, model upgrade workflows, safety guardrails, and production
Testing
RAG (Retrieval-Augmented Generation) systems fail in ways that don't look like failures. The answer is fluent, confident, and wrong. The retrieved context was relevant but the LLM ignored it. The retrieved context was irrelevant and the LLM hallucinated around it. Without systematic testing, these failures reach users invisibly.
Testing
RAG evaluation frameworks exist because manual inspection doesn't scale. You can't read 10,000 (question, answer, context) triples and judge whether the model is hallucinating, whether the retrieved context is relevant, or whether the answer actually addresses the question. RAGAS and TruLens automate this judgment using
Testing
Vector databases are infrastructure. Like any database, they can be misconfigured, queried incorrectly, or updated in ways that corrupt your index. Unlike relational databases, the failure modes are subtle: incorrect metadata filters silently narrow results, wrong namespace routing returns data from a different index, stale vectors from deleted documents still
Testing
Embeddings are the foundation of semantic search, RAG systems, and recommendation engines. When embedding behavior is wrong — whether because the model changed, the preprocessing changed, or the similarity calculation is buggy — everything downstream breaks silently. Search returns irrelevant results. RAG systems hallucinate. Recommendations get weird. Testing embeddings requires thinking about