Stable-version caret ranges
CaretBasin is a Windows coding client for discussing stable-version caret intervals. Open the project and review a compatible model’s proposed numeric bounds without treating range admission as proof of compatibility.
def caret_admits(base, candidate):
for version in (base, candidate):
if type(version) not in (list, tuple) or len(version) != 3 or any(type(x) is not int or x < 0 for x in version):
raise ValueError("version.triplet")
major, minor, patch = base
upper = (major+1, 0, 0) if major else ((0, minor+1, 0) if minor else (0, 0, patch+1))
return tuple(base) <= tuple(candidate) < upperProject
Check next-major and both zero-major boundaries, rejecting Boolean or fractional components without promising software compatibility.
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 caret_admits(base, candidate):
for version in (base, candidate):
if type(version) not in (list, tuple) or len(version) != 3 or any(type(x) is not int or x < 0 for x in version):
raise ValueError("version.triplet")
major, minor, patch = base
upper = (major+1, 0, 0) if major else ((0, minor+1, 0) if minor else (0, 0, patch+1))
return tuple(base) <= tuple(candidate) < upperMake version assumptions part of the change request
CaretBasin is Windows AI coding software for reviewing project changes involving version rules and dependency assumptions. Select the repository folder and explain the compatibility behavior you need. Ask the connected model to inspect the relevant source and configuration before proposing edits, so the conversation separates an application's own version logic from the package tools it happens to use.
The caret_range.py example suggests a task about admitting a candidate version within a bounded range. Bring cases for a stable major version and a zero-major version, along with the expected acceptance decisions. Ask whether the caller relies on those distinctions. This turns the example into a checkable software task without claiming that the workspace manages upgrades or implements every package manager's range syntax.
For unrestricted AI model choice, configure an available model through a supported OpenAI-compatible or Anthropic endpoint. Required credentials, tools and context limits depend on that service. Inspect the resulting patch and check cases immediately below and at each boundary. If configuration changes accompany the code, run the project's normal dependency and test workflow as well. The useful outcome is a compatibility decision you can explain, rather than an upgrade accepted simply because the assistant suggested it.
Reviewing version-range behavior
Does the example understand full package version syntax?
The example concerns numeric version triplets. Do not assume it handles prereleases, build metadata or every range expression. Specify the syntax your application actually accepts and review the parsing boundary separately from the comparison logic.
Why include zero-major versions in the request?
Their intended compatibility boundary may differ from stable major versions. Provide explicit candidate versions and expected outcomes so the model does not generalize from a single stable-version example. Review those decisions against the rules your application has chosen.
Should a range fix also update dependencies?
Keep those as separate decisions unless the task requires both. A comparison bug can be corrected without changing installed packages. If an upgrade is necessary, inspect the dependency changes and run the existing project checks before accepting the combined patch.
How do I test an exclusive upper boundary?
Include the last accepted representative case and a candidate exactly at the upper limit. Also check the lower boundary and a lower rejected version. State expected results independently so a mistaken comparison cannot define its own passing tests.
CaretBasin · Updated 2026-10-25