For Developers

Welcome to the Hahaha developer community!

This section is for all developers who want to contribute. Whether you are an experienced engineer or just getting started, there is a place for you here.

Welcome

Hahaha is an open-source machine learning library project. We focus on:

  • Education-first: make learning ML more intuitive and fun
  • High performance: modern C++ implementation, multiple compute backends
  • Code quality: clear, maintainable, and extensible design

Developer roles

Core contributors

  • Architects: participate in system design and technical choices
  • Core developers: implement and maintain core modules
  • Test engineers: ensure quality and stability

Community contributors

  • Feature developers: implement new features and improve existing ones
  • Documentation maintainers: improve docs and examples
  • Problem solvers: fix bugs and performance issues

Newcomer contributors

  • Example writers: create usage examples and tutorials
  • Test contributors: add unit tests and integration tests
  • Translators: provide multilingual documentation

Development environment

Operating systems:

  • Linux (Ubuntu 20.04+, Fedora 34+, Arch Linux)
  • macOS (10.15+)
  • Windows (WSL2 recommended)

Developer tools:

  • IDE: CLion, VS Code, Visual Studio
  • Compiler: GCC 11+, Clang 14+, MSVC 2022+
  • Build system: Meson + Ninja
  • Version control: Git

Setup guide

For detailed steps, see: How to Contribute

Code quality standards

Design principles

  1. Clarity first: code should be easy to understand; readability comes first
  2. Modularity: independent functionality, clear interfaces, easy to test
  3. Performance and safety: keep performance while ensuring memory safety
  4. Educational value: structure should reflect algorithmic ideas

Coding conventions

  • Language version: C++23
  • Naming: follow the project conventions
  • Docs: every public interface should have documentation comments
  • Testing: core functionality should have good unit test coverage

Contribution flow

Quick start

  1. Understand the project: read the project intro and architecture docs
  2. Pick a task: check the roadmap and TODO list
  3. Set up environment: configure your dev environment
  4. Start coding: follow the contribution guide

Workflow

Pick a task → Create a branch → Write code → Write tests → Open a PR → Review → Merge

For details, see: How to Contribute

Learning resources

Project docs

Developer resources

External resources

  • C++23 standard: get familiar with modern C++ features
  • ML fundamentals: understand the algorithmic basics
  • Open-source development: learn OSS collaboration workflows

Community and communication

Channels

  • GitHub Issues: bug reports and suggestions
  • GitHub Discussions: technical discussions and proposals
  • Pull Requests: code contribution and review

Guidelines

  1. Be respectful: friendly, constructive communication
  2. Stay problem-focused: focus on solving problems, not debating people
  3. Be open-minded: welcome different opinions and suggestions
  4. Contribute proactively: not only report issues, but also propose solutions

Recognition

Types of contributions

  • Code: features, bug fixes, performance improvements
  • Docs: writing, translating, and improving documentation
  • Tests: adding test cases and improving coverage
  • Design: architecture design, API design, technical research

How we recognize contributions

  • Contributors list
  • Authorship in code where relevant
  • Documenting design/implementation reasoning
  • Community acknowledgement

Growth path

Getting started

  1. Start by running and understanding existing examples
  2. Fix small issues and simple bugs
  3. Add tests for existing functionality
  4. Improve documentation and comments

Advancing

  1. Develop new modules and features
  2. Improve architecture and performance
  3. Build developer tools and scripts
  4. Help other contributors grow

Vision

We believe programming should be joyful and learning should be fun. Through Hahaha, we hope to:

  • Provide high-quality learning materials for developers
  • Build a friendly and open open-source community
  • Promote machine learning education
  • Create high-performance tools with educational value

Getting help

If you run into issues while developing:

  1. Read the docs: check relevant pages and the FAQ first
  2. Search issues: see if someone already reported something similar
  3. Ask: open a GitHub issue with details
  4. Reach out: the community will do its best to help

Let’s build a fun and education-oriented ML library together!

Last updated: Jan 2026