Canonical Roman numerals
NumeralHaven is a Windows workspace for developing code through an AI conversation and reviewing the resulting edits. A Roman-numeral formatter provides a compact project task where subtractive forms and the accepted numeric range can be inspected together.
def roman_numeral(n):
if type(n) is not int or not 1 <= n <= 3999:
raise ValueError("roman.range")
values = [(1000,"M"),(900,"CM"),(500,"D"),(400,"CD"),
(100,"C"),(90,"XC"),(50,"L"),(40,"XL"),
(10,"X"),(9,"IX"),(5,"V"),(4,"IV"),(1,"I")]
parts = []
for value, symbol in values:
count, n = divmod(n, value)
parts.append(symbol * count)
return "".join(parts)| Project | Check subtractive forms such as 4 and 900, rejecting zero, out-of-range numbers and non-integers. |
|---|---|
| Review before applying | Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes. |
| Connection | Base URL, credentials and a reachable server. |
def roman_numeral(n):
if type(n) is not int or not 1 <= n <= 3999:
raise ValueError("roman.range")
values = [(1000,"M"),(900,"CM"),(500,"D"),(400,"CD"),
(100,"C"),(90,"XC"),(50,"L"),(40,"XL"),
(10,"X"),(9,"IX"),(5,"V"),(4,"IV"),(1,"I")]
parts = []
for value, symbol in values:
count, n = divmod(n, value)
parts.append(symbol * count)
return "".join(parts)Give a formatter a contract its callers can rely on
NumeralHaven offers a Windows coding workspace for discussing implementation details with a connected AI model. Formatting work is a useful place to begin because a small function can carry visible product rules. Describe the accepted input and exact output before asking for changes, and include the code that presents the result to a user.
A Roman-numeral formatter is the supplied project example. You could ask the assistant to review its supported numeric range and subtractive forms, then explain how callers should handle rejected values. The point is to develop your own code with clear expectations, rather than assume the workspace provides a finished numeral-conversion service.
Once the contract is explicit, inspect whether the suggested edit changes output spelling, accepted types or failure behavior. Run fixtures around range boundaries and representative subtractive forms using your project tools. Also check the surrounding display path so an invalid value does not become an unexplained blank or a misleading label in the interface.
For model-assisted work, connect an OpenAI-compatible or Anthropic endpoint and supply credentials when required. An unfiltered AI model is an option if that service offers it. Its context capacity, tool availability and response restrictions remain specific to the server and model you selected; review remains part of the development workflow.
Before changing a value formatter
Why include the formatter's caller in the request?
The caller decides how a rejected value or formatted string reaches the interface. Include that path so the assistant can explain the effect of a changed return value or exception, not just the internal conversion.
What examples make a formatting requirement precise?
Provide accepted values with their exact expected strings, plus values that must be rejected. For the Roman-numeral task, include range boundaries and subtractive forms. Those fixtures make output conventions visible before any implementation is proposed.
Should a formatter silently accept numeric text?
Only if your application's contract requires that conversion. State accepted types explicitly and review where parsing belongs. A convenient coercion inside formatting can conceal a caller bug or change how invalid values reach the user.
Can a different model change the formatter's required output?
Changing the connected model should not change your stated requirements. Keep the same fixtures and acceptance rules when comparing proposals. Evaluate each edit against those rules rather than treating a model's preferred convention as authoritative.
NumeralHaven · Updated 2026-10-25