{
  "video_id": "8f7e44d8fdff1c9d",
  "channel_slug": "simonwillison-net-atom-everything",
  "channel_handle": "simonwillison-net-atom-everything",
  "title": "Qwen3.8-Flash-Next",
  "url": "https://simonwillison.net/2026/Aug/26/qwen38-flash-next/",
  "upload_date": "20260826",
  "published_at": "2026-08-26T23:52:58+00:00",
  "transcript": "Qwen3.8-Flash-Next Another open weights model from Qwen. This one is \"a multimodal MoE model that also serves as an early preview of the architecture used in Qwen4\". It's pretty big: 125B tokens, but only 6B active which means it gets a significant performance boost. I've been trying it out on a DGX Spark using these Unsloth quantized models . I'm still exploring the model - so far I've tried the 72.5GB UD-IQ1_S one (producing these pelicans ) and the 78.9GB UD-Q2_K_XL (producing these ). My favorite so far was this xhigh reasoning effort one from UD-Q2_K_XL: Via Hacker News Tags: ai , generative-ai , llms , qwen , pelican-riding-a-bicycle , ai-in-china , nvidia-spark",
  "transcript_chars": 676,
  "ingested_at": "2026-08-27T02:00:02.847639+00:00",
  "source": "rss",
  "yt_meta": {
    "author": null,
    "tags": [
      "ai",
      "generative-ai",
      "llms",
      "qwen",
      "pelican-riding-a-bicycle",
      "ai-in-china",
      "nvidia-spark"
    ]
  }
}