Open Source Friday: Mockoon – Run mock APIs locally

Key Concepts

GitHub issues from Copilot, Copilot Airways, product requirements document, Notion, Copilot coding agent, Model Context Protocol (MCP), native tools, code edits, builds, tests, external tools, GitHub Actions, pull request, code review, Makun, API mocking, open source, community building, maintainer burnout, GitHub accelerator, cloud product, sustainability, local first development, guard routes, CRUD routes, data buckets, microservices simulation, webhooks, API specification.

GitHub Copilot and Model Context Protocol (MCP) for Issue Creation

• Main Topic: Utilizing GitHub Copilot and the Model Context Protocol (MCP) to streamline issue creation and code development.

• Key Points & Process:

  1. Creating GitHub Issues from Copilot Chat: Users can initiate GitHub issues directly from Copilot chat within github.com.
  2. Real-world Example: Product team at "Copilot Airways" uses Copilot to assign work to the engineering team.
  3. Scenario: The product team has a new product requirements document for user reviews stored in Notion.
  4. Action: Copilot is instructed to create a GitHub issue with the relevant information and a link to the Notion document, ensuring traceability.
  5. Copilot Coding Agent: The agent is assigned the issue directly from Copilot chat and begins working on a pull request.
  6. Model Context Protocol (MCP): Copilot accesses external tools like Notion via MCP.
  7. MCP's Functionality: MCP allows Copilot to gather information from external sources (like Notion in this case) and incorporate it into its workflow.
  8. Configuration: MCP servers and associated tools are configured within GitHub Actions during Copilot setup.
  9. Code Review Process: Copilot summarizes the changes made in the pull request, aiding in efficient code review.
  10. Resources: More information available at gh.io/copilot/coding-agent.

• Technical Terms:

  • Model Context Protocol (MCP): A protocol that allows Copilot to access external tools and information sources.
  • Copilot Coding Agent: An intelligent agent within Copilot that can be assigned issues and create pull requests.

Makun: API Mocking Tool - Origins and Open Source Journey

• Main Topic: The origins, development, and community building around Makun, an API mocking tool.

• Key Points & Background:

  • Creator: Guom, co-founder and maintainer of Makun.
  • Guom's Background: Formerly a lawyer specializing in intellectual property and privacy. Switched careers to full-stack development.
  • Motivation for Makun: Created to address the need for an API mocking tool in a bank where cloud-based solutions like Postman couldn't be used.
  • Origins of Makun: Started as a side project. An MVP was created and shared on Hacker News and Reddit. Initial feedback was positive.
  • Initial Model: Originally not open source, possibly intended for a one-time purchase model.

• The Transition to Open Source:

  • Community Influence: A request from a user prompted the shift to open source.
  • Guom's Perspective: Initially saw open source as a marketing strategy, but evolved into a passionate advocate for open source.
  • Commitment to Open Source: Prioritizes open source principles, even above the success of Makun Cloud. "I would rather see the cloud fail than for example making Mochun cloud source closed."

Building and Engaging with the Open Source Community

• Main Topic: The challenges and strategies involved in building and maintaining a community around an open-source project.

• Key Points & Challenges:

  • Community Building Complexity: Acknowledges the difficulty in building a successful open-source community.
  • Initial Steps: Start with code, choose a license (MIT in Makun's case), and publish.
  • Maintenance Demands: Popularity leads to maintenance needs, bug fixes, support requests, and feature additions.
  • The Benevolent Dictator Role: Maintainers often become the primary decision-makers, addressing community needs and project direction.
  • Sustainability Issues: Balancing personal life, day jobs, and the unpaid work of maintaining an open-source project.
  • Burnout: Personal experience of burnout in 2021, leading to full-time commitment to Makun.

• Community Engagement:

  • Early Engagement: Answering requests, providing support, adding features, fixing bugs to engage the early community.
  • Content Creation: Writing content and opening a Discord channel.
  • Onboarding Challenges: Difficulties in finding and onboarding new maintainers due to commitment levels and code understanding.

The GitHub Accelerator and Funding for Open Source

• Main Topic: The impact of the GitHub Accelerator program and the challenges of funding mid-sized open-source projects.

• Key Points & Insights:

  • The GitHub Accelerator: A positive experience providing learning and networking opportunities.
  • Reflection: Forced reflection on project direction and the cloud-building strategy.
  • Funding Challenges: Mid-sized projects often struggle to secure public or research funding.
  • Balancing Act: Managing the open-source project with a commercial cloud product to achieve sustainability.

• Funding Strategies:

  • Donations and Sponsorships: Difficult to secure consistent and substantial sponsorship.
  • Building a Cloud Product: Developed Makun Cloud to address sustainability.
  • Independence: Aim to maintain independence and avoid raising funds.
  • License: Commitment to keeping the MIT license "as long as possible."

Balancing Open Source and Cloud Product

• Main Topic: The strategies for balancing the open-source project with the cloud product while maintaining community trust.

• Key Points:

  • Feature Parity: Aiming for the same set of features in both the local and cloud versions.
  • Cloud-First Approach: New features may appear in the cloud version first and then migrate to the local version.
  • Avoiding Paywalls: Attempting to avoid restricting local features behind paywalls.
  • Cloud-Specific Features: Paywalled features typically involve cloud-specific functionality like cloud deployments and real-time team collaboration.
  • Blog Posts: Documenting the journey and decisions related to balancing the open-source and cloud aspects.

AI and the Future of API Mocking

• Main Topic: Considering the role of AI in API mocking and potential future developments.

• Key Points:

  • AI Assistant: Existing AI assistant in Makun is a wrapper around ChatGPT, primarily for cloud features.
  • Concerns: Recognition of concerns about AI overuse and user preferences for optional AI integration.
  • Priorities: Current focus is on other roadmap items, including enterprise features for the cloud.
  • AI Limitations: Current limitations in AI's ability to handle complex logic and stateful mocking.

• Potential Developments:

  • AI-Driven Automation: Acknowledgment of potential for full automation of API mocking in the future.
  • MCP Integration: Consideration of Model Context Protocol (MCP) for AI-driven development.

Makun Demo - Simplicity and Power

• Main Topic: Demonstration of Makun's core features, highlighting its simplicity and powerful capabilities.

• Key Points:

  • Double Promise: Makun aims to be both simple to use and a powerful API mocking tool.
  • Local vs. Cloud: Differentiation between the desktop application (local) and the web application (cloud).
  • Quick Mock Creation: Ability to quickly create an API mock with minimal setup.
  • Cloud Deployment: Deploying mocks to the cloud in a few clicks.

• Advanced Features:

  • Guard Routes: Protecting routes and requiring API keys for access.
  • CRUD Routes: Quickly setting up CRUD operations for data buckets.
  • Data Buckets: Stateful JSON databases for storing and managing data.
  • Webhooks/Callbacks: Simulating microservice advertising.
  • Global Variables: Creating feature flag systems.

Local First Development and its Future

• Main Topic: Discussion on the importance and future of local-first development tools.

• Key Points:

  • Data Privacy: Local-first tools are essential for data privacy and testing, especially in regulated environments like banking.
  • Team Collaboration: Local-first tools often require cloud components for team collaboration.
  • Data Location: Important to know where the data is going and which jurisdiction it is subject to.
  • Exit Plan: Need for an exit plan when using cloud-based tools.
  • Feedback Loop: Local development followed by cloud collaboration forms a continuous feedback loop.

How to Ensure the Mock Behaves Like the Actual API

• Key Points:
  • Open API Specification: Importing API specifications to create mocks.
  • Limitations: Open API specifications do not define rules or dynamic behavior.
  • Maintenance: Requires manual maintenance of mocks to ensure they stay aligned with the actual API.

Makun Unique Features

• Key Points:
  • Free and Open Source: Makun's availability as a free and open-source tool.
  • Case Study: Impala case study - used for product API feedback during development.
  • Flexibility: Appealing to API developers, sales personnel, and designers.
  • No Code: No code environment allows people to spin up tests or demo's very quickly.
  • Team Collaboration: Real-time collaboration features in the cloud version.
  • Easy Cloud Deployments: Cloud deployments without the complexity of Docker.

Conclusion

Makun is a powerful and versatile API mocking tool designed to be both easy to use and highly customizable. It caters to a wide range of users, from developers needing quick local mocks to enterprises requiring cloud-based collaboration and realistic sandboxes. Its unique features, such as guard routes, CRUD routes, and scripting capabilities, combined with its commitment to open source and local-first development, position it as a valuable asset in the API development landscape. The integration of AI and the careful balance between the open-source project and the cloud product further enhance its long-term sustainability and relevance.