7dbb95fa15
One up-front LLM pass over the full transcript extracts a theme summary + terminology map, merges it under the user's manual glossary (user entries always win), caches it on the dub job per target language (job_data blob, no schema change), and injects the brief into every per-segment prompt. A new reflect pass then critiques each segment's direct translation for wordiness / stiff register and rewrites it as natural spoken dialogue — any failure or divergence silently keeps the direct translation. Both stages have Dub-tab toggles (default ON for the LLM engine, persisted; MT engines unaffected), with i18n strings across all 21 locales and docs updated. Co-authored-by: mergetest <test@local> Co-authored-by: Claude Fable 5 <noreply@anthropic.com>