{
  "version": "https://jsonfeed.org/version/1.1",
  "title": "AI News: Lilian Weng",
  "home_page_url": "https://ai-news.ypetit.net/sources/voice-weng/",
  "feed_url": "https://ai-news.ypetit.net/feed/source/voice-weng.json",
  "description": "Latest publications from Lilian Weng, aggregated by AI News from the official source.",
  "language": "en",
  "items": [
    {
      "id": "https://lilianweng.github.io/posts/2026-07-04-harness/",
      "url": "https://lilianweng.github.io/posts/2026-07-04-harness/",
      "title": "Lilian Weng: Harness Engineering for Self-Improvement",
      "summary": "The concept of recursive self-improvement (RSI) dates back to I. J. Good (1965) , where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself. Yudkowsky (2008) used the phrase “r…",
      "date_published": "2026-07-04T00:00:00.000Z",
      "tags": [
        "voices",
        "Lilian Weng"
      ],
      "authors": [
        {
          "name": "Lilian Weng"
        }
      ]
    },
    {
      "id": "https://lilianweng.github.io/posts/2026-06-24-scaling-laws/",
      "url": "https://lilianweng.github.io/posts/2026-06-24-scaling-laws/",
      "title": "Lilian Weng: Scaling Laws, Carefully",
      "summary": "Scaling laws are one of the most critical empirical findings in deep learning. The observation is simple in form: the training loss $L$ decreases predictably as we scale up model size $N$, dataset size $D$, and compute $C$, following a power-law curve, which appears as a strai…",
      "date_published": "2026-06-24T00:00:00.000Z",
      "tags": [
        "voices",
        "Lilian Weng"
      ],
      "authors": [
        {
          "name": "Lilian Weng"
        }
      ]
    }
  ]
}