The creator of Grand Coder reports on internal historical evaluations aimed at helping capable AI models reason and work more diligently, rather than making the model itself smarter. A tiered engineering study initially ran 96 isolated executions but excluded two task families due to invalid hidden requirements, leaving 72 runs for analysis.
- The baseline passed 13 out of 18 tasks, while three Grand Coder configurations passed 16/18, 16/18, and 15/18 respectively.
- Gains were concentrated in two retained task families, with the other four already passing in every condition.
- Other evaluations showed saturated tests with no difference or apparent wins that disappeared after scoring corrections.
- Blinded review scores improved in one build study, but corrected behavioral tests showed no advantage at higher cost.
The author concludes that Grand Coder has produced specific measurable benefits useful for ongoing work, emphasizing the need to separate reviewer impressions from actual behavioral checks and costs. The implementation remains private, limiting independent verification of these findings.