has anybody benchmarked these solution that claim to help you save tokens? hearing from friends that some of them are increasing their token spend
LLMS
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Wan-Streamer v0.1 Real-time Interactive Foundation Model
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Wan-Streamer v0.1
— AK (@_akhaliq) 25 juin 2026
End-to-end Real-time Interactive Foundation Models pic.twitter.com/5NYOEquaMqWan-Streamer v0.1 End-to-end Real-time Interactive Foundation Models
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CC/codex/opencode agents collaborating to improve Gemma 4
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Actually it was (CC/codex/opencode) agents collaborating to *improve* Gemma 4
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GPT-5.6-Preview Detected in ChatGPT Code for Enterprise Partners
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OPENAI : GPT-5.6-Preview has been spotted in the ChatGPT code. It was likely made available to certain partner Enterprises too. This also potentially means that it will remain in a limited preview state for some time. Not soon?
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Tapered LMs: Early layers write more, later layers refine
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“Tapered Language Models” Most LMs give every layer the same MLP width, but the paper shows this is probably wasteful. Early layers seem to write more new information into the residual stream, while later layers mostly refine what is already there. So instead of making the
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Help Eyal critique running LLM inference in browser
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who wants to help eyal poke holes in this approach to run LLM inference… in browser?
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Google reorganizes AI coding strike team into midtraining group
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Google is reorganizing its AI coding strike team as it tries to close the gap with Anthropic in one of the most lucrative parts of the AI market. According to The Information, the months-old team is being expanded into a more formal "midtraining" group, sitting between
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DFlash: Drop-in Speculative Decoding for SGLang, vLLM, TensorRT-LLM
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/7 Drop-in for SGLang, vLLM, and TensorRT-LLM. No code refactoring. SGLang:
–speculative-algorithm DFLASH
–speculative-draft-model-path z-lab/Qwen3-8B-DFlash-b16 vLLM: via the Speculators library (
http://
docs.vllm.ai/projects/specu
lators
…, algorithm "dflash") MIT license. ICML 2026 accepted. -
Two metrics: paper speedup and NVIDIA
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/4 Two metrics. 1. Paper (single-stream lossless latency, Qwen3-8B, Transformers backend): > Average 4.86x speedup over autoregressive baseline
> Peak 6.08x on MATH-500 (τ = 7.87 average acceptance length)
> 2.5x higher than EAGLE-3 at matched draft budget 2. NVIDIA -
DFlash extracts hidden features from future tokens in LLMs
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/5 The insight is from Samragh et al. (2025): large autoregressive LLMs already encode information about multiple future tokens in their hidden states. The target model is doing work that the drafter never gets to see. DFlash taps that. It extracts hidden features from uniformly