{
  "claims": [
    {
      "text": "Trend detected: Local-First AI with SLMs is seeing a sustained increase in builder adoption.",
      "source_uri": "gemini://trending-ai/edge-slm-optimization",
      "debug_coordinate": "line:12,char:1"
    },
    {
      "text": "Operator attention is clustering around production usage rather than one-off experimentation.",
      "source_uri": "gemini://trending-ai/edge-slm-optimization",
      "debug_coordinate": "line:13,char:1"
    },
    {
      "text": "Why it matters now: The rise of highly capable sub-10B parameter models is enabling sophisticated local-first application architectures without cloud dependency.",
      "source_uri": "gemini://trending-ai/edge-slm-optimization",
      "debug_coordinate": "line:14,char:1"
    },
    {
      "text": "Privacy-conscious builders are migrating core inference tasks to on-device small language models for lower latency. Recent breakthroughs in quantization allow 7B models to run efficiently on mobile and embedded hardware. The developer community is focusing on hybrid orchestration where the cloud only handles the most complex reasoning tasks.",
      "source_uri": "gemini://trending-ai/edge-slm-optimization",
      "debug_coordinate": "line:16,char:1"
    }
  ],
  "format": "zigg.ing-anythingllm-bundle-v1",
  "sources": [
    {
      "title": "Local-First AI with SLMs",
      "excerpt": "Trend detected: Local-First AI with SLMs is seeing a sustained increase in builder adoption. Operator attention is clustering around production usage rather than one-off experimentation. Why it matters now: The rise of…",
      "trace_id": "6589eba6-6b45-4816-b665-dfc9ccdb2006",
      "source_uri": "gemini://trending-ai/edge-slm-optimization",
      "raw_signal_id": "a5f23661-40e8-4fac-a2c0-249fa20368b0"
    }
  ],
  "summary": "Why this matters This artifact is grounded in 4 verified claims across 1 context signals. Corroborating evidence: Trend detected: Local-First AI with SLMs is seeing a sustained increase in builder adoption; Operator attention is clustering around production usage rather than one-off experimentation. The trend path emphasizes sustained movement over isolated events so downstream Zigs are less sensitive to one-off noise.",
  "headline": "Local-First AI with SLMs",
  "artifact_id": "900d7f1f-d166-491d-b9f7-ad26d86ec2e7",
  "raw_sources": [
    {
      "sha256": "e190edb6389dc79e72a535c2ef6752d5e08a266d206cc1f46506c2c4d0d1c913",
      "trace_id": "a5f23661-40e8-4fac-a2c0-249fa20368b0",
      "mime_type": "text/plain",
      "source_uri": "gemini://trending-ai/edge-slm-optimization"
    }
  ],
  "workspace_name": "zigg-edge-slm-optimization"
}