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V3 also added adaptive speed control. Rather than blindly holding at 16x, the script monitors the audio element’s buffered time ranges to assess buffer health. If the buffer ahead of the playback position is shrinking (meaning the network can’t keep up with the decode speed), the playback rate is reduced to give the fetcher time to catch up. If the buffer is healthy and growing, the rate is nudged back up. This prevents the browser from stalling entirely on slow connections, which would previously break the ended event trigger and leave you waiting forever.

GlyphNet’s own results support this: their best CNN (VGG16 fine-tuned on rendered glyphs) achieved 63-67% accuracy on domain-level binary classification. Learned features do not dramatically outperform structural similarity for glyph comparison, and they introduce model versioning concerns and training corpus dependencies. For a dataset intended to feed into security policy, determinism and auditability matter more than marginal accuracy gains.

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