BottleCap AI released ThinkingCap-Qwen3.8-27B as a fine-tune of Qwen3.8-27B to shorten reasoning traces. It cuts thinking tokens by 37.2% on average across 12 benchmarks while macro-average accuracy drops from 86.65% to 85.79% (0.86pp). It’s deployable as a drop-in on vLLM or SGLang with various quantized builds, but weights are gated and commercial use needs a BottleCap agreement beyond the small-business license.
Claude.ai’s core website and desktop app experience was sped up by about 3x over a two-week sprint, after users reported it was slow. At the 75th percentile, time to reach a typeable page on a fresh claude.ai load dropped from 3.1 seconds to 0.55 seconds. The team used Claude Tag with lab benchmarks and CI guardrails, shipping thousands of performance changes with flags and then ratcheting benchmarks to keep improvements in production.
Researchers compressed streaming neural audio tokenizers by distilling a teacher’s continuous pre-quantizer latent into a student encoder instead of matching discrete tokens or output distributions. At 2.8× compression, the student stayed within 1.9% relative WER of the teacher on 5 of 6 pairs without fine-tuning. The result is a single distillation recipe that works across token interfaces and yields better accuracy than an independently trained tokenizer at the same capacity.
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