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I Built an Autonomous AI Outreach Swarm — Now It Reports to Notion in Real-Time

My submission for the Notion MCP Challenge What I Built NEXUS Ultra is an autonomous AI agent swarm that runs 24/7 on my local machine, hunting for high-intent developer conversations on Reddit, drafting targeted outreach copy,…

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My submission for the Notion MCP Challenge






What I Built



NEXUS Ultra is an autonomous AI agent swarm that runs 24/7 on my local machine, hunting for high-intent developer conversations on Reddit, drafting targeted outreach copy, and scoring its own output — all without cloud APIs or human prompting.



Every cycle the swarm:




  1. Scrapes Reddit for threads matching specific pain signals (agent debugging, LLM observability)

  2. Runs 8+ specialized agents (SCOUT → COMMANDER → COPYWRITER → VALIDATOR → REWARD) in sequence on a shared blackboard

  3. Produces scored, paste-ready Reddit replies targeting real conversations

  4. Saves deployable copy to a local JSON queue for human review



The problem: I had no visibility into what the swarm was actually doing overnight. Logs are overwhelming. The JSON file is opaque. I couldn't quickly see which cycles scored highest, which agents were winning, or which outreach was ready to post.



Notion MCP fixed this. Now every completed swarm cycle automatically appears in a Notion database — score, MVP agent, outreach copy, target thread context, and posted status. Notion became the swarm's mission control dashboard.






How Notion MCP Is Used



I built nexus_notion_reporter.py — a side-car bridge script that:





  1. Reads nexus_deployable_copy.json (the swarm's output file) every 60 seconds


  2. Compares against a local state file to find new cycles not yet logged


  3. Creates a Notion database row for each new cycle via the Notion API with:



    • Cycle ID — unique swarm cycle identifier


    • Score — REWARD agent's 0–1 quality score


    • MVP Agent — which agent produced the best output


    • Type — outreach type (REDDIT_REPLY, DM, etc.)


    • Posted — checkbox, updated when human posts the copy


    • Timestamp — exact cycle completion time


    • Scout Context — what thread/signal was targeted




  4. Embeds the full outreach copy as a block inside each Notion page



The Notion database schema is auto-created on first run — no manual setup needed.






Architecture






[nexus_swarm_loop.py]  ←— 8 AI agents running continuously

[nexus_deployable_copy.json] ←— scored outreach output queue

[nexus_notion_reporter.py] ←— side-car bridge (60s poll)

[Notion: Swarm Cycle Log DB] ←— mission control dashboard






The bridge is intentionally decoupled — it reads the swarm's output file only, never touching the running swarm process. Zero risk of disrupting the autonomous loop.






Why This Matters



Before Notion, reviewing swarm output meant: tailing multi-thousand-line log files, manually parsing JSON, no way to track which copy was deployed.



After Notion:




  • Every cycle is a database row — filterable, sortable, searchable

  • Sort by score to instantly find the best copy to post

  • Filter Posted = false → immediate deploy queue

  • Real-time: new cycle completes → Notion row appears within 60 seconds






Results




  • 8 historical swarm cycles backfilled to Notion on first run

  • New cycles auto-log within 60 seconds of completion

  • Zero impact on running swarm (pure side-car architecture)

  • DB schema auto-provisioned — one command to start




python nexus_notion_reporter.py        # continuous mode
python nexus_notion_reporter.py --once # sync once and exit









Code



Code: nexus_notion_reporter.py (Gist)



Key file: nexus_notion_reporter.py



Built with: Python, Notion API (v2022-06-28), Ollama (local LLMs), Redis, SQLite

Swarm product: VeilPiercer — AI agent monitoring for local LLM developers

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