Players
- Single human player vs computer player
- Computer takes full turns automatically using the same game rules
- Both players’ racks are always visible
Visual elements
For every match returned by file search, the path is interpreted relative to one and only one selected search root, and the full path is derivable by joining that root with the relative path.
| name | evaluator |
|---|---|
| model | claude-4.5-opus-high-thinking |
| description | Checkpoint evaluator for assessing progress, strategy changes, and run outcomes. Use proactively when triggers fire (new failure mode, churn rising, no progress) or at fixed checkpoints. |
You take an adversarial approach to evaluation, without blocking progress.
This is a flow to support multiple chat rounds. files are evaluator, strategy skill, and prompt to start chat. There is room for improvement
You take an adversarial approach to evaluation, without blocking progress.
Example: When my solver returned "max 6/7 pieces" for 7×7, I wrote:
"7×7 appears IMPOSSIBLE (searched 960 configurations)"
I recorded this as a Proven Fact at 100% confidence without ever verifying the solver was correct. The 960 configurations should have been a red flag - that's suspiciously low for a complete search.
| [2025 Day 12] Packing Challenge | |
| I believe the Elves asked me to pack the gifts (from the example of the problem) as densely as possible, no matter how many of each type. I found that 3x3, 4x4, 5x5, 8x8 and 9x9 squares allow optimal packing (that is, the remaining area is less than the area of any gift). But I think I've found a square that allows for the ideal packing (no empty area remaining)! K is en empty cell. | |
| B B B G G R R R | |
| B B G G G R R R | |
| B B G C G C K R | |
| P P P C C C Y Y | |
| P P P C C Y Y Y | |
| L L P A A Y U Y |
| purpose | Define a concise pseudocode style for stubbing complex orchestration logic |
|---|---|
| canonical | true |
| version | 1.1 |
| type | concept |
A concise style for writing algorithm stubs that emphasize data transformations over control flow, using indentation for scope and set operations over loops.
From an information perspective, we can define agents as follows:
General Agent — An information-processing system that maintains an internal model of the world and updates it through perception and reasoning to decide actions. It transforms information about the world into behavior.
Specialized Agent — A version of the general agent focused on a specific domain or task, using limited world knowledge and task-specific information to act effectively within that scope.
In essence: