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MonkeyCode is an enterprise-level AI development platform launched by Chaitin Technology, dedicated to providing developers with a more professional, reliable, and scalable AI Coding experience. MonkeyCode is not just an AI programming tool, but an AI R&D infrastructure for professional teams. It covers the entire process of Requirements → Design → Development → Review, providing a secure, isolated, and parallelizable development environment. * * * ## Registration and Login **For first-time use of MonkeyCode, please complete the account registration first. Click (https://monkeycode-ai.com/?ic=019d94af-c5d0-7207-a923-89d7ccf67d91), register or log in directly at the top right corner. After success, you will automatically enter the main interface to start using it.** After entering the page, click **Register** in the top right corner to create a new account; if you already have an account, you can log in directly. !(#) After registration is complete, the system will automatically redirect to the main interface, requiring no additional operations. !(#) As you can see, there are many Skills available here: !(#) ### Create a Simple Application Online To test an application, enter the following content: Create an online tool for work traceability records, recording daily work progress, and establishing a complete work trajectory archive It also provides many models, and MiniMax2.7 is still free: !(#) Next, it will start the creation process: !(#) After creation is complete, access it directly online: !(#) The effect is as follows: !(#) Add feature test: !(#) The effect is as follows: !(#) * * * ## Configuration Instructions Before executing specific tasks, you need to complete the basic platform configuration, including the binding operations for repositories, large models, system images, and host machines. The configuration entrance is located in the lower left corner of the platform. ### 1. Git Platform Credential Binding You must first create a code repository on the corresponding Git platform, and then select the target platform in MonkeyCode to complete the credential binding. Ensure that the repository address and permission configuration are accurate, providing basic support for subsequent code operations. !(#) Note that the account must have public repositories to perform the binding. ### 2. Large Model Binding **Default Recommendation:** If there are no special business requirements, you can directly use the platform's built-in free model MiniMax-M2.7. This model was selected after comprehensively comparing domestic mainstream large models on the platform, offering balanced performance and strong adaptability. Professional Edition Models: Provides various models such as gpt, minimax, glm, kimi, mimo, deepseek, etc. Using them will consume account credits. Click on credits in the lower left corner to recharge. !(#) **Custom Binding:** If you need to use other large models, you can fill in the model API Token in the corresponding configuration module to complete the binding. It supports the access adaptation of mainstream large models. !(#) Just fill in the API token here. ### 3. System Image and Host Binding, Development Environment Creation For projects with specific runtime environment requirements, you can configure an independent development environment through this module, adapting to project demands of different tech stacks and system versions: Select the target environment template and initialize the environment configuration: !(#) Confirm the environment parameters and complete the creation of the personalized development environment: !(#) !(#) * * * ## Executing Tasks The platform supports two task initiation methods. You can choose according to the actual scenario. The core goal is to drive code development, debugging, and other operations through natural language requirements. The Basic Edition supports running 1 task simultaneously, while the Professional Edition supports running 3 tasks simultaneously. !(#) ### Initiation Method 1: Direct Input of Requirements Accurately describe the task requirements in the main interface dialog box (such as feature development, Bug fixes, code optimization, etc.), and you can select a matching skill template based on the project type. After submission, the platform will automatically parse the requirements and execute them. !(#) ### Initiation Method 2: Bind Repository Project to Execute * Select "Add Project" in the left sidebar; * Confirm the target repository address and branch information (ensure the repository address is configured correctly, and the platform account has sufficient repository operation permissions); * Submit the task requirements, and the platform will execute the operation based on the specified repository branch. !(#) After selection, you can see the repository-related information. After modifying the configuration, click "Start AI" to begin the task. !(#) Before starting, select the model and environment to use. The free version defaults to minimax2.7, and the professional version can switch to more models. !(#) * * * ## Platform Usage Instructions ### Interface Interaction Instructions **Real-time Interaction:** During task execution, you can supplement requirements and adjust instructions in the dialog box. The platform will respond in real-time and optimize the execution results; the execution time can be seen in the lower right corner, and the upper left corner contains other function buttons. !(#) **Progress Visualization:** During task execution, the interface will display detailed execution steps and the time consumed by each step. The right panel allows you to view