Review before applying
Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
Small monetary units deserve explicit arithmetic. DeltaMoss gives basket calculations, currency rules and edge cases a shared project context while you work with a compatible AI endpoint.
if (a.currency !== b.currency) throw new Error("currency");
if (![a.minor, b.minor].every(Number.isSafeInteger)) throw new Error("amount");
const minor = a.minor + b.minor;
if (!Number.isSafeInteger(minor)) throw new Error("range");
return { currency: a.currency, minor };Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
Base URL, credentials and a reachable server.
Test zero items, a refund and the largest supported sum, rejecting mixed currencies before any addition.
if (a.currency !== b.currency) throw new Error("currency");
if (![a.minor, b.minor].every(Number.isSafeInteger)) throw new Error("amount");
const minor = a.minor + b.minor;
if (!Number.isSafeInteger(minor)) throw new Error("range");
return { currency: a.currency, minor };DeltaMoss provides a Windows coding workspace for discussing application changes where numbers carry a defined meaning. Start with the project folder and the behavior you need to preserve. A useful conversation names the data representation, its callers and the result expected by the interface, so a suggested edit can be reviewed beyond its arithmetic expression.
The basket example in money.ts is one concrete task. Amounts have a currency and an integer number of minor units, and a proposed addition must respect both. Ask the model to inspect each operand check and the result check, then examine how the caller handles rejection. This is example project code, not a payment engine built into DeltaMoss.
Keep business decisions visible while reviewing that proposal. A mixed-currency basket needs an explicit application policy; a helper should not silently invent conversion rates. Run relevant project checks for ordinary totals, unsupported amounts and values near the accepted range. Inspect the user-visible failure path as carefully as the successful total.
For assistance, connect a supported OpenAI-compatible or Anthropic endpoint using credentials when the service requires them. Abliterated AI models can be selected where available, subject to their actual provider behavior. Context capacity and tool access depend on the chosen model and server, and do not establish the correctness of a monetary calculation.
Only if conversion is an explicit requirement backed by a defined source and rounding policy. The shown task rejects mismatched currencies. Keep a currency-conversion feature separate so a small arithmetic fix does not introduce unstated pricing behavior into the application.
An invalid operand can still produce a plausible result. Check the accepted representation before adding, then check the sum against its own bounds. Review fixtures where a fractional or unsupported value combines into a seemingly valid integer total.
Describe the error the caller receives and the action available to the person using the application. Test that rejected arithmetic cannot be displayed as a successful checkout total. The right message and recovery behavior depend on your existing checkout design.
No. Use it to identify assumptions worth checking, then inspect the code and run independent examples in your project. A confident explanation does not prove range handling, representation choices or compatibility with the callers that consume the amount.
DeltaMoss · Updated 2026-10-25