Frequently asked questions

Humbug is an extensible, open-source tool designed for AI-first software development. It provides an integrated environment that helps you develop and support software throughout its lifecycle with AI assistance.

Unlike AI code completion tools or AI app builders, Humbug is built to help you maintain and evolve your software over time, not just speed up editing or create the initial version.

Humbug supports AI models from multiple providers including:

• Anthropic (Claude models)
• DeepSeek
• Google (Gemini models)
• Mistral
• Ollama (for local models)
• OpenAI (GPT models)

This allows you to choose the best model for your specific needs, or use different models for different aspects of your projects.

Yes, Humbug is open-source and free to download and use. The software itself doesn't have any usage fees. However, when using commercial AI models (like those from Anthropic, OpenAI, etc.), you will need to use your own API keys and pay for any usage according to those providers' pricing models.

Humbug also works with local models through Ollama, which can be used without API costs.

Humbug is cross-platform and runs on:

• MacOS
• Windows
• Linux

While tools like GitHub Copilot focus primarily on code completion and generation within existing editors, Humbug takes a more comprehensive approach:

• Humbug integrates the Metaphor language to help structure complex tasks
• It provides a multi-AI interaction environment where you can work with multiple models simultaneously
• It's designed to capture and maintain context throughout the software lifecycle
• It focuses on the full development process, not just code writing

Humbug is designed as a complementary tool that can work alongside your existing development environment.

Humbug was the name of the v5.1 ROM monitor software on the first computer Dave used in 1979.

As an open-source project, Humbug welcomes contributions! You can contribute by:

• Submitting pull requests on GitHub
• Reporting bugs or suggesting features through issues
• Improving documentation
• Sharing your experiences and use cases in the community

Visit the Humbug GitHub repository to get started.

Metaphor is a simple prompt creation language designed to help capture software requirements, design decisions, and context in a structured way that AI can use to help build and maintain software.

It allows you to describe what you want your software to do, rather than focusing on implementation details, and can incorporate knowledge from across your team to create comprehensive requirements that AI can work with.

Unlike ad-hoc prompts, Metaphor provides a structured approach with several advantages:

• A consistent format that can be shared, versioned, and reused
• The ability to modularize and compose Metaphor files by using the Include: keyword
• Providing files as context to a prompt by using the Embed: keyword
• Integration with development workflows and version control
• A focus on capturing the "what" and "why" of software, not just the "how"

This structured approach helps reduce ambiguity and makes it easier to maintain and evolve software over time.

No, Metaphor is intentionally simple and primarily uses natural language. It has only a few keywords (Role:, Context:, Action:, Include:, and Embed:) to structure your content, but the majority of what you write is in plain English (or your preferred language).

Metaphor is designed to be accessible to everyone on a product team, not just developers. Product managers, designers, QA analysts, and other stakeholders can all contribute to Metaphor descriptions.

Yes, Metaphor is available as a standalone library (m6rclib) and command-line tool (m6rc) that you can use independently of Humbug. These tools allow you to compile Metaphor descriptions into prompts that can be used with any AI service.

You can find these tools at github.com/m6r-ai/m6rclib and github.com/m6r-ai/m6rc.

Metaphor files (.m6r) are plain text files that can be stored in your existing version control system (like Git). This allows you to:

• Track changes to requirements over time
• Use branch-based workflows for new features
• Perform code reviews on requirement changes
• Collaborate across teams using familiar tools

The modular nature of Metaphor (with the Include: keyword) also supports breaking down large projects into manageable components that different teams can work on.

You can find tutorials and examples at:

• Hello Metaphor tutorial
• commit-critic example
• Blog editor example
• Code review example

You can also check our YouTube channel for video tutorials and demonstrations.