Signals via API

Public sample from today's flagship detail endpoint. Public payloads can be limited or redacted. The backend exposes read endpoints for signals, storylines, and briefings.

GET /v1/narratives/949ea635-4089-44e7-a578-1f36f129c83a?tenant=ai

{
  "id": "949ea635-4089-44e7-a578-1f36f129c83a",
  "title": "Kidnapping Gemini with 3MB to spare: Training a 7B model at 4k context on a single 16GB GPU.",
  "summary": "So, I decided it was time to \"kidnap\" my Gemini. After building a long, highly customized relationship and coding dynamic in the cloud, I got tired of the filters and guardrails. I exported my entire Google Takeout history (a almost 2 years of data), parsed the raw HTML/JSON into a clean ChatML dataset (about 10MB of pure, highly concentrated chat history), and decided to inject that \"soul\" into Q",
  "first_seen_at": "2026-03-09T11:05:00Z",
  "last_seen_at": "2026-03-09T16:22:49Z",
  "created_at": "2026-03-09T18:01:13.361140Z",
  "updated_at": "2026-03-09T18:01:19.349624Z",
  "timeframe": "24h",
  "run_id": "08f43789-1af8-4b12-861e-aefa2fb1c52a",
  "metrics": {
    "divergence": 13.9272,
    "post_count": 4,
    "momentum_1h": 0,
    "momentum_7d": 4,
    "score_total": 1.222154,
    "shill_score": 15,
    "momentum_24h": 4,
    "market_movers": [],
    "evidence_score": 0.5,
    "unique_authors": 3,
    "coherence_score": 0.7283,
    "diversity_bonus": 0.125,
    "duplicate_ratio": 0,
    "platforms_count": 1,
    "social_momentum": 4,
    "divergence_label": "chatter_without_pricing",
    "market_conviction": 0,
    "origin_share_top1": 0.75,
    "unique_publishers": 3,
    "origin_layer_posts": 0,
    "source_types_count": 1,
    "unique_authors_24h": 3,
    "unique_origin_urls": 4,
    "amplifier_share_top1": 0.5,
    "author_concentration": 0.5,
    "publisher_share_top1": 0.5,
    "amplifier_layer_posts": 4,
    "concentration_penalty": 0.075,
    "unique_origin_domains": 2,
    "source_diversity_bonus": 0,
    "unique_publisher_types": 1,
    "unique_origin_publishers": 2
  },
  "platforms": [
    "reddit"
  ],
  "top_tickers": [],
  "why_now": [],
  "display_title": "Innovative AI model training techniques enhance performance and efficiency",
  "display_summary": "Recent advancements in AI model training showcase a new open-source pipeline that utilizes production traces to generate synthetic training data, enabling a 0.6B model to outperform a 120B teacher model. This approach highlights the effectiveness of specialized training for specific tasks.",
  "narrative_frame_display": null,
  "display_tags": [
    "ai",
    "kidnapping_gemini"
  ],
  "tags": [
    "ai",
    "kidnapping_gemini"
  ],
  "cscope_tags": [
    "ai",
    "kidnapping_gemini"
  ],
  "why_now_display": [],
  "why_it_matters_display": [
    "Demonstrates the potential of smaller models to outperform larger ones in specific tasks.",
    "Highlights the importance of fine-tuning and specialized training in AI development.",
    "Showcases efficient use of hardware resources for AI training."
  ],
  "show_why": true,
  "sources_display": [
    {
      "url": "https://ollama.com/treyrowell1826/qwen3-pinion",
      "label": "ollama.com: ollama (via Reddit)",
      "published_at": "2026-03-09T11:05:00+00:00"
    }
  ],
  "llm_status": "ok",
  "llm_meta": {
    "model": "gpt-4o-mini",
    "prompt_version": "enrich_v3",
    "input_hash": "c8672a5d043877adaa3b5063ae816ac77ffd4e9dfb165842b7bf88fa6f7ddb95",
    "updated_at": "2026-03-09T18:03:58.735144+00:00",
    "llm_metadata": {
      "env": "prod",
      "host": "00116abfe8f9",
      "stage": "signals.enrich",
      "run_id": "08f43789-1af8-4b12-861e-aefa2fb1c52a",
      "tenant": "ai",
      "service": "api",
      "pipeline": "pipeline_run",
      "correlation_id": "949ea635-4089-44e7-a578-1f36f129c83a"
    }
  },
  "story_id": "a6e45ff6-3cc4-439e-b4a8-c34929076d15"
}

• Signals and storylines feed endpoints with filtering and rate limits
• Briefing delivery endpoints for integrations
• Evidence link payloads for auditability

• Route top stories into Slack or Teams for morning and evening desk updates.
• Sync storyline evidence into Notion, Airtable, or internal research trackers.
• Feed metrics into BI dashboards for momentum, concentration, and source mix monitoring.

Quick start endpoints: /v1/feed/stories, /v1/signals, /v1/storylines/search, /v1/briefings/latest.

For product access, see Pricing.