mastodon.timeline_tag

curl "https://api.onekey.dev/v1/mastodon/timeline_tag?tag=python" \
  -H "x-api-key: YOUR_API_KEY"

{
  "success": true,
  "data": [
    {
      "content": {
        "id": "117350344281766094",
        "url": "https://www.urbanmind.net/display/f7dd981d-cec99446-d49183579dfadbe9",
        "text": "I originally thought customer memory was a storage problem. It turned out to be a retrieval and trust problem. When I built FUEGO, a customer relationship memory agent, I wanted one simple behaviour: before a customer meeting, the system should remember what happened before without forcing someone to reconstruct the relationship from scattered meetings, tickets, and notes. The key design decision was separating structured customer records from long-term semantic memory. SQLite remains the source of truth for customers, meetings, and support tickets. Hindsight handles the historical context that needs to be retrieved when a question is asked. Groq then turns that context into a useful response. That separation made the rest of the system much easier to reason about. The architecture: FUEGO uses a Next.js frontend and a Python/FastAPI backend. The frontend lets a user select a customer, add meetings and support tickets, prepare for a meeting, inspect commitments and solutions, and ask questions. The backend connects SQLite, Hindsight, and Groq. SQLite stores concrete records. Hindsight provides long-term customer context. Groq uses the retrieved context to generate the final response. I wanted those responsibilities to stay separate. A database row is not the same thing as a memory, and a retrieved memory is not automatically a fact that should overwrite a structured record. Why ordinary customer history",
        "createdAt": "2026-09-28T19:06:23Z",
        "language": "en"
      },
      "author": {
        "id": "115061140460610278",
        "username": "hackaday",
        "displayName": "Hack a Day (unofficial)",
        "url": "https://www.urbanmind.net/profile/hackaday",
        "avatarUrl": "https://files.mastodon.social/cache/accounts/avatars/115/061/140/460/610/278/original/b7c7093473e2d110.png"
      },
      "engagement": {
        "likes": 0,
        "comments": 0,
        "shares": 3
      }
    },
    {
      "content": {
        "id": "117350295913913001",
        "url": "https://mastodon.social/@teeeeeeeee/117350295913913001",
        "text": "Boosted Configuration (_neural_) Networks for classification https:// thierrymoudiki.github.io/blog/ 2022/07/21/r/misc/boosted-configuration-networks # Python # DataScience # MachineLearning # rstats # Techtonique",
        "createdAt": "2026-09-28T19:23:31Z",
        "language": "en"
      },
      "author": {
        "id": "116862964066274332",
        "username": "teeeeeeeee",
        "displayName": "T@Techtonique",
        "url": "https://mastodon.social/@teeeeeeeee",
        "avatarUrl": "https://files.mastodon.social/accounts/avatars/116/862/964/066/274/332/original/7b6c651c404d5235.png"
      },
      "engagement": {
        "likes": 0,
        "comments": 0,
        "shares": 2
      }
    }
  ],
  "metadata": {
    "platform": "mastodon",
    "endpoint": "timeline_tag",
    "cached": false,
    "stale": false,
    "age_s": 0,
    "fetched_at": "2026-01-01T12:00:00+00:00",
    "credits_charged": 5,
    "request_id": "7f3a9c2e5b1d4c8fa2e6d0b9c4f18e37"
  },
  "error": null
}