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Faster long documents: Smart Humanize uses the fast engine at any length (FAST_MAX_WORDS default 0; a 22,000-word part took 13 min on the sentence-by-sentence engine, the fast engine reached 0% in about 4); report-guided runs at 0% stop when a round rewords fewer than 3 (or 5%) of the highlighted sentences left (5 of 12 minutes went on taking 80 to 72); wallet usual cost of long fast runs from live measurements; pricing table rows all on the fast engine
unknown +1: Claude Opus 5.5Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>d14dbe0
6 days ago
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Fast engine: never uses a rewrite the faithfulness check did not approve; spending cap scaled to length; stops at 0%
unknown +1: Claude Opus 5.5- A version is used only if the faithfulness check said it keeps the meaning. When the check could not run (the flat $1 job cap stopped it in a 5,500-word test), sentences keep their wording instead of taking unchecked text. - groqai.set_cap: per-job spending cap; the fast engine allows $0.60 per 1,000 words (at least GROQ_JOB_BUDGET). - Once the document is at 0%, one more round at most (for leftover stock phrases). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>f66d9ce -
One-minute deadline for the fast engine; half-precision detector on the GPU when it gives the same verdicts
unknown +1: Claude Opus 5.5- fast.py: the whole job ends within FAST_BUDGET (52 s) whatever the length: language-model requests still running at the deadline are dropped (their sentences keep their best version), rounds are planned from measured times, and long documents' candidates are pre-ranked on the GPU too. - detector.py: half precision on the GPU (about 2x faster), switched on at start-up only if it changes no verdict and no score by more than 0.05 on the bundled samples; logged either way. DETECTOR_HALF=0 = never. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>439ec70
7 days ago
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Fast engine: versions from several models at once (Gemini 3.5 Flash-Lite + Qwen 3.8 by default, gpt-oss-20b as fallback), checked inside the worker threads; generator benchmark
unknown +1: Claude Opus 5.5Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>01f5654 -
Fast engine: every flagged sentence replaced at once (versions judged on their own), 16 sentences per request, keep-every-detail prompt applied in the worker threads, step timings
unknown +1: Claude Opus 5.5Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>d46df30 -
Fast engine (opt-in with ?engine=fast) and one-step document upload
unknown +1: Claude Opus 5.5- fast.py: the whole document at once. The detector reads it once; every flagged sentence is rewritten in parallel by the language model (keep-every-detail prompt); versions must keep terms / numbers / citations, the meaning (embeddings + an independent faithfulness reading) and read as fluent English; the detector picks the most human version in context. Rounds repeat on what is still flagged within a time budget (FAST_BUDGET, 50 s). Live engine unchanged unless SMART_ENGINE=fast or the page is opened with ?engine=fast. - Smart Humanize opens on the Word upload, and dropping a file starts the run; the separate Humanizer tab is hidden. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>4a7a056