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Claude vs Cursor vs Github vs OpenAI. I've explained each feature below. 1. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 This shows how independently the AI can complete real tasks. • The AI can plan steps instead of waiting for every instruction • It can search files, edit code, and finish tasks end-to-end • The goal becomes more important than individual prompts Claude currently feels closest to a fully autonomous AI engineer. ______________ 2. 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 This decides whether the AI learns your project over time. • The system remembers past context and decisions • It understands project structure and patterns • Work becomes faster as the project grows Without memory, the AI starts from zero in every session. ______________ 3. 𝗠𝘂𝗹𝘁𝗶-𝗙𝗶𝗹𝗲 𝗥𝗲𝗳𝗮𝗰𝘁𝗼𝗿𝘀 Real software lives across hundreds of files. • The AI updates many files safely at once • Large changes can be applied across the codebase • Refactoring becomes faster and less risky Strong refactoring support means the AI can handle real production projects. ______________ 4. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗔𝗴𝗲𝗻𝘁𝘀 This is where AI starts behaving like a team. • Multiple agents can work on different tasks simultaneously • Bugs, features, and tests can be handled together • Development speed increases dramatically This is a glimpse into the future of AI-driven development. ______________ 5. 𝗡𝗮𝘁𝗶𝘃𝗲 𝗧𝗲𝗿𝗺𝗶𝗻𝗮𝗹 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 This decides whether the AI can actually run your project. • Install packages and dependencies • Run builds and test suites • Execute scripts and debug environments This moves AI from a coding assistant to a true operator. ______________ 6. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗟𝗲𝗻𝗴𝘁𝗵 This is the amount of code the AI can understand at once. • Larger context means deeper project understanding • Architecture and relationships become clearer • Large codebases become manageable Bigger context = smarter decisions. ______________ 7. 𝗖𝗜/𝗖𝗗 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 AI is now part of the software delivery pipeline. • Helps with deployments and workflows • Supports automated testing and releases • Integrates with modern development pipelines AI is no longer just writing code, it helps ship it. ______________ 8. 𝗠𝗼𝗱𝗲𝗹 𝗦𝘄𝗶𝘁𝗰𝗵𝗶𝗻𝗴 This shows how flexible the tool ecosystem is. • Some tools allow switching between different AI models • Others focus on deep integration with one ecosystem • The choice depends on flexibility vs simplicity Both approaches serve different types of teams. Have a look at the table below for comparison. ✅ 𝗜𝗻 𝘀𝗵𝗼𝗿𝘁: • 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 → Best for large and complex codebases • 𝗖𝘂𝗿𝘀𝗼𝗿 → Best daily companion for full-time developers • 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 → Best for teams working inside GitHub workflows • 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 → Best for general coding, learning, and automation ✅ Repost for others who are using or planning to use these tools.