I _really_ hesitated to include _any_ printed output that wasn't a warning for an invalid setting. However, maybe even _more_ valuable than the ultimate factorization output, with `FACTOR_FINDER` method, is the running list of smooth numbers successfully identified in the sieve. Based on empirical tests (or theory that isn't yet apparent to me), the script `wheel_tuner.py` in the project root can be used with this intermediate output, of a list of smooth number, to better tune the application of wheel factorization.
**Full Changelog**: https://github.com/vm6502q/FindAFactor/compare/v6.6.0...v6.7.0
sha1sum results:
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4c4d85167e4d8ed1299135948808162c4f155035 findafactor-6.7.0.tar.gz