AI prototypes often feel fast at first, then suddenly slow down as usage grows. Caching is one of the simplest ways to improve performance, reduce costs, and make prototypes feel responsive without rewriting everything. This article introduces caching concepts for vibe coders, including what to cache, where to cache it, and common pitfalls like stale data or incorrect assumptions about freshness.
AI prototypes often start small, but as they handle multiple API calls, model inference, or large computations, they can become slow or unresponsive. Threading and parallelism let your prototype do more than one thing at a time, improving speed and responsiveness. This article explains the difference between threading (concurrent tasks in a single process) and parallelism (tasks across multiple cores or processes).
Synchronous vs asynchronous execution is one of the most important architectural concepts vibe coders overlook. Choosing the wrong one can make your prototype slow, unresponsive, or unexpectedly complex. In this article, you’ll learn the core differences between sync and async work, why async is not “faster” by default, when each approach makes sense, and how AI-powered prototypes often rely on both.
