We validated our COBOL-to-Python engine on 15,552 real-world programs. 98.78% produce valid Python. Zero LLMs involved.

We validated our COBOL-to-Python engine on 15,552 real-world programs. 98.78% produce valid Python. Zero LLMs involved. Last week we published a proof of concept with IBM's SAM1 — 505 lines, 32 milliseconds. This week we scaled it to the entire planet. The corpus 15,552 COBOL source files. Not synthetic benchmarks. Real programs, collected from 131 open-source repositories across 5 continents: — Norway. France. Brazil. India. Japan. USA. — GitHub. HuggingFace. CBT Tape. GnuCOBOL. IBM public repositories. — Commercial COBOL. GnuCOBOL extensions. TypeCOBOL. Mainframe dialects. No selection bias. No curated samples. Everything we could find. The result Before (v5.6) Corpus Valid Python Failures Net gain 14,508 files 14,020 (96.84%) After (v5.8e) 15,552 files (+1,044) 15,362 (98.78%) 456 — 190 +1,342 files On the original v5.7 reference corpus: 99.25%. 180 of 289 failures corrected in a single session. What "valid Python" means We are not using LLMs to judge output quality. We are not doin