Engineering notes
Ideas behind AI Alignment Infrastructure.
An editorial on our approach, engineering practices, and research methods. Each develops a question behind the platform beyond its product description.
Scaling enterprise expertise for AI development
Why preparation and interpretation determine whether expert participation can scale, and how that work connects to rubrics, coverage, harnesses, and weights.
Read the articleWhen does a coding-agent skill generalize?
Assess applicability, accepted changes, and unfamiliar tasks when developing skills and harnesses.
Read the practiceTeach the distinction behind the benchmark failure
Connect expert criteria to training-data augmentation, then examine whether the gain holds on independent assessment cases.
Read the practiceThe rule an expert has never written down
Why concrete comparisons, professional context, and unresolved disagreement matter when eliciting a standard.
Read the methodCoverage is a design problem
Separate the scope a benchmark should represent from the examples already available. Keep the purpose of each case explicit.
Read the methodWhat would falsify an improvement claim?
Distinguish a better candidate from a changed test, and examine regressions alongside aggregate gains.
Read the method