A developer shares their frustrating experience with Cursor AI, where the majority of development time (5.5 out of 6 hours) was spent correcting the AI's mistakes and dealing with unresponsive behavior. The post highlights current limitations of AI-assisted coding tools and suggests they aren't yet mature enough for efficient development.
Always review and verify code generated by AI tools before implementation
Maintain alternative development approaches when using AI tools
Avoid complete dependence on AI tools for code generation without manual oversight
Avoid assuming AI coding tools will handle all programming tasks effectively
A developer discusses productivity challenges with Cursor's AI composer, specifically the context-switching issues during code generation wait times. The post highlights how brief AI inference delays can lead to workflow interruptions and distractions, similar to traditional compilation time issues but with more potential for distraction.
A developer shares a simple but effective two-step prompting strategy for working with AI coding assistants, specifically Cursor. The approach involves requesting an overview before any code generation, which helps catch misunderstandings and requirement gaps early in the development process.
A developer shares their positive experience using new .mdc cursor/rules files for improved code generation in Convex projects. The implementation demonstrates significant improvement in one-shot code generation compared to previous methods, reducing the need for multiple prompts and showing enhanced effectiveness over traditional documentation-based approaches.
A software engineer and dev agency owner shares their experience using Cursor AI over two months, breaking down the strengths and limitations of three main features: Cursor Agents, Composer, and Chat. The post provides practical guidelines for when to use each feature effectively, based on real-world project implementation experience.
A detailed comparison of three major AI coding tools (Bolt, v0, and Cursor) based on hands-on experience. The analysis covers each tool's strengths, limitations, and ideal use cases, with particular focus on their applicability for different skill levels and project types. The post emphasizes the importance of actual coding skills while leveraging AI tools for enhanced productivity.