AxisLoom is a Windows AI coding client for working through matrix transformations in source files. Discuss row ownership and shape checks in the project context, then inspect the suggested transpose implementation before applying it.
def transpose(rows):
rows = [list(row) for row in rows]
if not rows:
return []
width = len(rows[0])
if any(len(row) != width for row in rows):
raise ValueError("matrix.ragged")
return [[row[column] for row in rows] for column in range(width)]def transpose(rows):
rows = [list(row) for row in rows]
if not rows:
return []
width = len(rows[0])
if any(len(row) != width for row in rows):
raise ValueError("matrix.ragged")
return [[row[column] for row in rows] for column in range(width)]Changing a data shape can leave each individual value intact while changing what the application thinks that value means. AxisLoom is a Windows AI coding workspace for examining such transformations with your project files available. Describe the incoming structure, the structure the next component expects and the point where they differ. Ask the assistant to trace that boundary before proposing edits.
A rectangular matrix transposition is one example you could review. The task might be to preserve column identity when preparing data for another component, while rejecting rows with inconsistent lengths. Ask how empty input is represented and inspect the consumer's assumptions about the result. Keep the proposal tied to your application's data contract; the sample does not establish a general import pipeline or automatic schema conversion service.
Abliterated AI models can be selected when offered through your supported OpenAI-compatible or Anthropic endpoint. Credentials may be required, and tools, restrictions and context depend on the model and server. Review the changed transformation and its caller together, then run representative project fixtures through both sides. If inference uses a separately running local server, that choice alone does not make the rest of the connected tools local.
Use a small structure with distinct values so their new positions are obvious. Label what rows and columns mean in the application. Repeated sample values can conceal a transposition or indexing mistake that otherwise looks like a valid result.
Only when padding is part of your contract and its meaning is defined. Rejecting inconsistent input may be appropriate. Ask the assistant to inspect current callers before introducing filler values that consumers could mistake for real data.
State the exact result your caller expects and how it distinguishes empty data from invalid data. Check the existing implementation and tests for that convention. Avoid changing the representation merely because another form looks simpler in isolation.
Yes. A consumer can interpret dimensions or positions differently even when the transformation follows its own rule. Review both ends and execute an example through the consuming component, checking meaning as well as the arrangement of values.
AxisLoom · Updated 2026-10-25