{
  "version": "https://jsonfeed.org/version/1.1",
  "title": "DIBS",
  "home_page_url": "https://getdibs.io/",
  "feed_url": "https://getdibs.io/feed.json",
  "description": "A personal publication about local AI, the things I build, and what's worth your time.",
  "items": [
    {
      "id": "https://getdibs.io/essays/local-model-tests/",
      "url": "https://getdibs.io/essays/local-model-tests/",
      "title": "Testing local models on my own machine",
      "summary": "Twelve coding problems, a speed test and two full app builds against local models, and what the failures showed. Every number is a recorded test result rather than a model’s account of its own work.",
      "date_published": "2026-10-20T12:00:00Z",
      "tags": [
        "local-ai"
      ]
    },
    {
      "id": "https://getdibs.io/notes/next-concept-modeling/",
      "url": "https://getdibs.io/notes/next-concept-modeling/",
      "title": "Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation",
      "summary": "A Stanford lab's alternative to writing one token at a time: encode text as a hierarchy of codes, then write one level at a time with every code in a level in parallel. The reported speedup is 10 to 31 times over diffusion baselines.",
      "date_published": "2026-10-20T12:00:00Z",
      "tags": []
    },
    {
      "id": "https://getdibs.io/essays/where-my-tokens-go/",
      "url": "https://getdibs.io/essays/where-my-tokens-go/",
      "title": "Where my Claude Code tokens actually go",
      "summary": "11.5 billion tokens across 14 Claude Code sessions in under eight weeks. On my biggest project the agent read about 1,550 tokens for every one it wrote. The rest was the conversation being sent again, and that is how the bill is built.",
      "date_published": "2026-10-20T12:00:00Z",
      "tags": [
        "claude-code"
      ]
    }
  ]
}