TripleMistral gives integer enumeration work a focused AI conversation beside your files. Review proposed loop limits and primitive-triangle conditions against a small independent fixture before accepting a patch.
from math import gcd
def primitive_triples(limit):
if type(limit) is not int or not 0 <= limit <= 500:
raise ValueError("hypotenuse.limit")
result, m = [], 2
while m * m + 1 <= limit:
for n in range(1, m):
c = m * m + n * n
if c <= limit and (m - n) % 2 and gcd(m, n) == 1:
a, b = sorted((m * m - n * n, 2 * m * n))
result.append((a, b, c))
m += 1
return sorted(result, key=lambda t: (t[2], t[0], t[1]))Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
Base URL, credentials and a reachable server.
Check the empty result and 3-4-5, exclude scaled copies and return each leg-ordered triple once.
from math import gcd
def primitive_triples(limit):
if type(limit) is not int or not 0 <= limit <= 500:
raise ValueError("hypotenuse.limit")
result, m = [], 2
while m * m + 1 <= limit:
for n in range(1, m):
c = m * m + n * n
if c <= limit and (m - n) % 2 and gcd(m, n) == 1:
a, b = sorted((m * m - n * n, 2 * m * n))
result.append((a, b, c))
m += 1
return sorted(result, key=lambda t: (t[2], t[0], t[1]))TripleMistral provides a Windows AI coding workspace for developing small computational components that need to fit a larger application. Start with the expected result and the limits of the input, then ask the assistant to inspect the project code that will call the component. A useful coding conversation covers return shape and failure behavior as well as the calculation that produces the values.
A task based on triples.py could examine a bounded enumeration of primitive Pythagorean triples. Ask the model to explain how the chosen limit and result ordering relate to your caller, then review a proposed correction with a few hand-checkable examples. The point is not to turn the homepage into a mathematical lesson; it is to show how a precise requirement becomes a component you can inspect and test.
Run your project checks around the smallest meaningful inputs and the accepted upper bound. Confirm that duplicates and ordering match the contract before using the output elsewhere. TripleMistral can connect to supported OpenAI-compatible or Anthropic endpoints, including an unfiltered model where one is offered. Credentials may be necessary, and the selected model and server determine policies, context capacity and tools. Keep verification separate from the model description: a confident explanation does not establish that an enumeration is complete.
Document whether order is part of the contract or merely an implementation detail. If consumers display or compare the sequence, test the required ordering explicitly before accepting a refactor that changes how results are accumulated.
Choose small inputs whose expected results you can enumerate independently and compare the full output. Review the generation conditions as well, because a duplicate-removal step can conceal repeated work or an underlying rule that is wrong.
Distinguish valid inputs that have no results from inputs outside the supported contract. Tell the assistant which should be rejected, then test the caller response so invalid parameters do not silently look like a successful empty computation.
It provides a concrete development task, not a catalog of built-in tools. Bring your own project and requirements, inspect the proposal and use checks appropriate to the component and the environment where it will run.
TripleMistral · Updated 2026-10-25