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Shotgun Code: Open-Source Tool for AI-Assisted Code Editing

Key Concepts:

• Shotgun Code: An open-source tool for preparing code context for AI models and applying diffs.
• Diff format: A text-based format representing the differences between two sets of files.
• AI Studio (Google): A platform for using Google's AI models, including Gemini.
• Klein: A tool for applying diffs generated by AI models.
• Gemini 2.5 Pro/Flash: Google's AI models used for code generation and editing.
• Repo Prompt: A tool that allows you to instantly select a folder and it will just let you get an instant tree plus file dump in a predictable delimiter format.
• Rukodes's human relay mode: A tool that does basically the same thing as Shotgun Code.
• Dart: An AI-native project management tool.

1. Introduction to Shotgun Code

• Shotgun Code is presented as an open-source and free alternative to Pastemax, addressing Pastemax's limitations.
• Pastemax allowed copying application context to AI models like ChatGPT but lacked the ability to apply changes or provide diff-formatted responses.
• Shotgun Code aims to replicate the functionality of tools like Repo Prompt, providing exact diffs for applying changes.

2. Functionality and Features

• Context Preparation: Shotgun Code allows users to select a folder, generating a file tree and content dump in a predictable format.
• File Exclusion: Users can exclude files (e.g., node_modules, .git) via checkboxes.
• AI Model Integration: The prepared context can be pasted into AI models like ChatGPT, Gemini 2.5, or Cursor for tasks like refactoring, bug fixes, and documentation.
• Diff Application: The AI model's diff-style response can be pasted back into Shotgun Code, which splits the diff into sections. These sections can then be applied using tools like Klein with the Gemini model.
• Comparison to Other Tools: Shotgun Code is compared to Rukodes's human relay mode, highlighting the similar functionality.
• Technology Stack: Built on Vue and Go, making it fast and lightweight without relying on databases or cloud services.

3. Usage and Workflow

• Installation: Download from the release section or build from source.
• Interface: The interface features a sidebar for folder selection and a display of files for inclusion/exclusion.
• Prompt Composition: A "compose prompt" option allows users to specify the desired task for the AI, including custom rules or constraints.
• Prompt Types: Different prompt types are available (e.g., "dev" for editing, "architect" for high-level suggestions, "find bug" for bug fixing).
• AI Model Interaction: The prepared context and prompt are pasted into an AI model (e.g., Gemini 2.5 Pro).
• Diff Processing: The AI's response (in diff format) is pasted back into Shotgun Code.
• Applying Changes with Klein: The split diff sections from Shotgun Code are copied and pasted into Klein, along with the prompt to apply the diffs using the Gemini 2.5 Flash model.

4. Example and Demonstration

• The video demonstrates using Gemini 2.5 Pro to generate a code modification.
• The resulting diff is pasted into Shotgun Code and then applied using Klein with the Gemini 2.5 Flash model.
• The demonstration shows the successful application of the AI-generated changes.

5. Advantages and Limitations

• Advantages:
  • Cost-effective alternative to paid AI APIs (e.g., ChatGPT's GPT-3).
  • Facilitates complex bug fixes and code modifications.
  • Leverages the capabilities of free AI models like Gemini.
• Limitations:
  • Lacks a built-in "patch" option for directly applying changes (unlike Rukodes's human relay mode).

6. Dart Sponsorship

• Dart is introduced as an AI-native project management tool.
• Features include AI-powered task generation, duplicate detection, and task assignment.
• Dart integrates with AI clients and coders via its MCP server.
• Most features are free, with a paid subscription available for additional functionality.

7. Conclusion

• Shotgun Code is presented as a valuable tool for AI-assisted code editing, particularly for users seeking cost-effective solutions.
• The tool's ability to prepare code context and process diffs makes it a useful complement to AI models like Gemini.
• The lack of a built-in patch option is noted as a potential area for future improvement.