Most developers approach financial apps as a purely mathematical problem: Minimize interest at all costs.
In technical terms, this is the Avalanche Method. You sort a list of debts by interest rate in descending order and allocate surplus cash to the top. Mathematically, it is the most efficient path.
However, when I was building TinyDebt, I realized that code that is “mathematically correct” often fails the “human test.” This led me to implement the Snowball Method sorting by balance size and the technical implementation taught me a lot about user-centric logic.
<h3>The Logic: Sorting for Success</h3>
In a local-first React Native environment, handling these calculations without a backend means the client-side logic must be snappy and reliable. Here is a simplified version of the logic I used to allow users to toggle between “Mathematical Efficiency” and “Psychological Momentum.”
<pre><code>type Debt = {
 id: string;
 name: string;
 balance: number;
 interestRate: number;
};

// The &quot;Avalanche&quot; Logic: Maximizing Interest Savings
const getAvalancheOrder = (debts: Debt[]) =&gt; {
 return [...debts].sort((a, b) =&gt; b.interestRate - a.interestRate);
};

// The &quot;Snowball&quot; Logic: Maximizing Psychological Wins
const getSnowballOrder = (debts: Debt[]) =&gt; {
 return [...debts].sort((a, b) =&gt; a.balance - b.balance);
};
</code></pre>
<h3>The Technical Challenge: Real-Time Comparison</h3>
The real value for the user isn’t just seeing a list; it’s seeing the impact of their choice. In TinyDebt, I had to build a projection engine that runs both algorithms against the user’s monthly “extra” payment to show two different “Debt-Free Dates.”
Because the app is local-first (using a simple, privacy-focused data model), these calculations happen instantly. There’s no loading spinner while a server calculates the amortization schedule. The result is a UI that feels responsive and encouraging.
<h3>Why “Snowball” Wins the UX Battle</h3>
While the Avalanche method saves more money in the long run, the Snowball method has a higher “completion rate.”
In programming, we call this reducing friction. * Avalanche is like refactoring a massive, complex legacy module first. It’s the right thing to do, but it’s exhausting.
Snowball is like fixing three “low-hanging fruit” bugs on Monday morning. It gives you the dopamine hit needed to tackle the big stuff on Tuesday.
<h3>Conclusion: Design for Humans, Not Just Databases</h3>
When we build tools, especially in the finance space, we have to account for the “human variable.” By providing both options in a minimalist, private environment, I found that users stay engaged much longer.
If you’re building a utility app, ask yourself: Is my logic optimized for the processor, or for the person using it?

Vladislav Siumbeli

A developer shares how they implemented both the Snowball and Avalanche debt payoff algorithms in their React Native app TinyDebt. The post covers the sorting logic for each method, a local-first projection engine that computes debt-free dates client-side, and the UX rationale for offering both options — arguing that psychologically motivating design (Snowball) often outperforms mathematically optimal design (Avalanche) in real-world user engagement.

The Math of Motivation: Implementing Snowball vs. Avalanche Algorithms in React Native