Rewards

Reward Philosophy

Traditional open-source compensation is either non-existent or based on subjective decisions. Edin takes a different approach: rewards are mathematically derived from objective evaluation data.

The key principle is compounding value. Contributors who deliver high-quality work consistently over time earn exponentially more than those who contribute sporadically. This reflects the reality that sustained engagement creates more value than one-off contributions.

Contribution Scoring

Each contribution receives a composite score based on three inputs:

• AI Evaluation Score — the objective quality assessment from the AI engine (complexity, quality, test coverage, impact).
• Peer Feedback Score — aggregated feedback from community members who reviewed the work.
• Task Complexity — the difficulty level of the task or contribution type.

These three inputs are weighted and combined into a single contribution score that feeds into the reward model.

Scaling-Law Compounding

The reward model uses mathematical scaling laws to distribute rewards across multiple time horizons. This means that your rewards are not just based on individual contributions but on your trajectory of contributions over time.

The multi-temporal distribution means rewards are calculated at different intervals — weekly, monthly, and quarterly — with each time horizon having its own weight. Longer periods reward consistency more heavily.

For a detailed explanation of the mathematical model, visit the Reward Methodology page.

Tracking Your Trajectory

The Dashboard > Rewards > Trajectory page provides a visual representation of your reward trajectory over time. You can see:

• Your cumulative reward score over time
• The compounding effect of sustained contributions
• Projected future trajectory based on current velocity
• Comparison with community benchmarks

Publication Rewards

Published articles generate additional rewards. The reward split follows an 80/20 model: 80% goes to the article author(s) and 20% goes to the editor who reviewed and approved the article. This incentivizes both content creation and editorial quality.

Article view counts also factor into reward calculations, meaning that articles with higher community engagement generate more value for their authors and editors.