- Connection
- Base URL, credentials and a reachable server.
- Project
- Try an occupied square, a move by the wrong player and a move after the game has ended, preserving the board on every rejection.
- Review before applying
- Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
AI coding workspaceKeep game-state changes consistent with the rules
Game logic becomes difficult to review when a move changes state before the application decides whether it is allowed. Turnvector offers a Windows coding workspace for examining those decisions with a connected AI model. Open your project, describe the action a player takes and state the conditions under which it should succeed. Ask for a trace through the existing implementation before requesting new behavior.
The illustrative board-move checks provide a starting point for one such task. You could examine turn ownership, occupied cells and finished games, then request a change that leaves rejected moves without an unintended state update. Read the proposed diff alongside the code that applies a legal move. These are requirements for your game's implementation, not a rules engine or game simulation supplied by Turnvector.
Abliterated AI model choice means selecting an available model through a supported OpenAI-compatible or Anthropic endpoint, not overriding its provider's policies. Supply credentials if required; context and tools depend on that model and server. After reviewing edits, run the project's rule tests and try sequences containing both valid and invalid moves. Check the resulting board and turn together, rather than accepting a plausible explanation as proof.
Questions for a game-rule revision
How should I describe a rejected move?
Give the starting board, active player, attempted cell and expected unchanged state. Include the reason for rejection. A precise before-and-after expectation makes it easier to assess whether the proposed code modifies anything before all required checks finish.
Should I ask the model to redesign every game rule?
Begin with the move or transition you need to change. Identify shared rule helpers before widening the task. Review the effect on existing callers and tests so a narrow defect does not become an unplanned redesign.
What should follow a successful single-move test?
Exercise sequences where valid moves surround rejected ones, including actions after the game ends. Compare board state and turn ownership after each action. Use your project's test environment; the workspace does not certify game correctness.
Can the selected model run a complete match by itself?
That depends on available tools and the project environment; do not assume it. You remain responsible for reviewing execution and results. A separate local model server also does not make every connected tool or service local.
Get the Windows app ↗Turnvector · Updated 2026-10-25