Explain why MagicQuant finds optimizations in the README opening - #17
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The README now explains the optimization opportunity immediately: tensor groups respond differently to compression, their interactions matter, and existing quantization recipes can supply useful combinations for a particular model.
The opening connects that opportunity to MagicQuant's process—learning tensor assignments, measuring group changes, predicting promising hybrids, benchmarking real GGUFs, and exporting measured results and clone manifests. A short numbered walkthrough makes the process approachable before the nonlinear-win example and published results.
Existing research, benchmark tables, support information, and setup guidance remain in place. This changes README prose only.
Validation: all eight Python documentation/release tests passed;
git diff --checkpassed.