A developer conducted a practical comparison of three leading AI models (DeepSeek, OpenAI o1, and Gemini) by building three different applications using Cursor AI. The experiment involved creating a mood tracking app, recipe generator, and whack-a-mole game, with DeepSeek achieving the highest performance score of 77.66%. The analysis provides detailed insights into each model's strengths and weaknesses for different use cases.
Choose AI models based on specific project requirements rather than overall performance scores
Don't rely on a single AI model as a universal solution
Don't base your entire development strategy on a single AI model
Avoid selecting models purely based on performance metrics without considering budget implications
A developer shares their positive experience using Cursor AI for code generation, highlighting how it allows them to focus on higher-level architectural decisions rather than implementation details. The post discusses the balance between AI-assisted development and manual coding, emphasizing that while AI code generation might not be suitable for critical systems, it's highly effective for typical business applications.
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 user shares a valuable tip for improving code generation quality in Cursor AI by explicitly requesting it to ask clarifying questions. The post highlights how adding a simple prompt rule can prevent hallucinated code and lead to more accurate, contextually appropriate code generation through interactive refinement.
The post emphasizes the importance of using Git version control when working with Cursor AI to safely experiment with code changes. The author encourages developers to leverage Git's checkpoint system as a safety net, allowing them to explore different approaches and revert changes if the AI-generated code doesn't meet expectations.
A developer shares their 4-month experience using Cursor Pro, detailing specific workflow optimizations and challenges. The post covers successful strategies like .cursorrules optimization, debug statement usage, and context management, while also highlighting limitations with less common technologies like Firebase/TypeScript, SwiftUI, and Svelte 5.