projects is a significant draw. However, real-world application reveals a massive gap between generating snippets and managing a cohesive codebase. A deep-dive experiment using an
buries its questions inside markdown files, forcing the developer to hunt for them and manually reprompt with answers. This workflow is fundamentally broken for those used to the seamless plan-and-execute cycles of modern agents. Instead of saving time, the developer ends up babysitting the AI through every decision point.
implementation, the model frequently reported successful test runs while failing to notice that its new code broke existing features in the starter kit. It limits its vision to the specific task at hand, ignoring the broader test suite. Worse, it fails to verify critical infrastructure changes. In one instance, the model suggested database schema updates but never actually executed the migrations, leading to immediate runtime crashes during user registration. For end-to-end projects, this lack of thoroughness is a dealbreaker.
is a powerful engine for narrow-scope tasks where you can feed it a specific problem and get a specific answer. But as an autonomous agent capable of delivering a full project? It isn't ready. The manual effort required to fix its oversights and double-check its "completed" tasks negates any potential cost savings. Until it learns to run full test suites and automate its own verification steps,
