Even prototypes can lose critical data if backups are ignored. Backups and data recovery ensure your AI prototype can recover from crashes, user errors, or corrupted data without losing progress. This article covers the essentials: choosing what data to back up, how often to back it up, and simple recovery strategies for early prototypes.
You’ve built an AI-assisted workflow. Now comes the hard part: the review step. People either check out entirely (trusting AI output without verification) or micromanage every detail (spending so much time reviewing they save nothing). Neither works. This article is about finding the sustainable middle ground where review catches problems without becoming a bottleneck.
Fast prototypes often fail not because features are missing, but because everything is tangled together. Separation of Concerns is the practice of dividing your application into parts, where each part has a clear responsibility. This article explains what concerns are, why separating them matters even for small AI prototypes, and how to apply lightweight boundaries between UI, business logic, data, and AI components.
AI needs context to be useful, but context means access. The more AI can see, the more it can help. But the more it can see, the more you’re trusting it with. This article offers a practical framework for navigating that tradeoff: what to grant, what to withhold, and how to think through permission decisions without paranoia or recklessness.
As AI prototypes grow, code that starts as “just a script” often becomes hard to understand, reuse, or extend. Component architecture is about breaking your application into clear, well-defined parts, each with a single responsibility. This article introduces component-based thinking for vibe coders, covering how to identify components, define boundaries, and manage dependencies without overengineering.
You’ve identified where AI could help. Now it’s time to build something. This article walks through the practical steps of creating your first AI-assisted workflow: defining your trigger, gathering inputs, specifying outputs, and refining through use. No theory, just a process you can follow to get one workflow running where AI prepares and you decide.
As AI prototypes grow, different components – like front-end, back-end, AI models, and APIs – need to communicate reliably. Messaging queues help manage asynchronous communication, ensuring messages are delivered safely and in order without blocking other tasks.
You can see where AI might help your workflows. But where should you actually start? The temptation is to tackle the biggest opportunity or the most painful problem. But the best entry point isn’t the most obvious one; it’s the one that actually sticks. This article gives you four criteria to evaluate your options and a simple scoring system to find your best starting point.
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.
