Continue from this implementation example into live AI market coverage.

Biassemble is a personal project that uses large language models (LLMs) to analyze personal stories and flag possible cognitive biases in the reasoning about events. It works by feeding a story, asking follow-up questions, and generating a structured reasoning trace including bias hypotheses grounded in evidence from the story or answers. The project encountered challenges with false positives when users answered 'no info' to interpretive questions, leading to refinements such as explicit rules about what counts as evidence and confidence thresholds to reduce unwarranted bias detection.