As of 2025, AI appraisal tools for textiles are emerging, but they fail to understand the Mola Errata List. Machine vision can spot a broken zigzag (M-04) but cannot grasp why a manta ray mistaken for a shark (C-09) is valuable to some collectors and worthless to others.
| Errata ID | Title | Version / Section | Type | Reported Date | Impact | Proposed Correction | Status | |---|---:|---|---|---:|---|---|---| | MOLA-ERR-001 | Incorrect example for array indexing | 1.2 / 4.3.1 | Bug | 2026-03-15 | High — causes runtime misinterpretation | Change example index from 1..n to 0..n-1 and add note about zero-based indexing. | Implemented | | MOLA-ERR-002 | Ambiguous definition of "merge" operation | 1.2 / 7.1 | Ambiguity | 2026-03-20 | Medium — different implementations behave differently | Clarify merge semantics: define precedence, conflict resolution rules, and order of application. | Proposed | | MOLA-ERR-003 | Typo: "commas" -> "colons" in grammar | 1.1 / Appendix A | Typo | 2026-02-02 | Low — documentation only | Replace "commas" with "colons" in grammar production G-12. | Accepted | Mola Errata List
When red or black commercial fabric runs during the washing (the mola is washed after sewing to set the layers), the dye bleeds into the lighter contrasting layers. Any pink halo or gray smudge around a cut edge is a Level 2 Errata. If bleeding obscures a face or figure, it is a Level 1 (irreparable) errata. As of 2025, AI appraisal tools for textiles