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Cover image for Project: X-STRUCT — Grok-guided Structure Engine for X
Peace Thabiwa
Peace Thabiwa

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Project: X-STRUCT — Grok-guided Structure Engine for X

#programming #startup #github #testing
  • We build an X posting brain that learns your topics, formats, timing, and audience loops.
  • Grok handles topic mapping, post composition, and feedback learning.
  • The system runs a collect → reason → compose → schedule → learn loop, with hard guardrails for TOS.

1) Problem → What we’re fixing

X’s algo rewards consistent themes, high reply density, and watch-time (threads/clips). Your volume is crazy (W), but the structure isn’t fully taught to the algo yet.

Goal: enforce a structured signal — topic clusters, repeatable post formats, and timed distribution — so the algo “understands” & pushes you.

2) System Overview (modules)

  1. Collector
  • Pulls your posts, replies, engagement via X API (paid tier) or exports.
  • Normalizes into event logs (post_id, topic, format, time, CTR).
  1. TopicGraph
  • Grok clusters posts → topics, links related posts → motifs.
  • Outputs a Theme Map (e.g., Binflow, MindsEye, Temporal AI, Cloud Flow).
  1. Composer
  • Grok generates post variants per topic + format (1-liners, contrasts, hooks, Qs, threads).
  • Injects visual slots (your Sora clips) every N posts.
  1. Scheduler
  • Staggers across 3 daily windows, ensures reply-first runs, and A/B tests.
  • Respects rate limits & TOS, batches via queue.
  1. FeedbackLoop
  • Reads engagement after 1h/6h/24h → updates topic/format weights.
  • Promotes winners to Evergreen Bank for reformatting.
  1. Guardrails
  • Safety filters, de-duplication, link rotation, throttles.

3) Data schemas (polyglot, easy to wire)

events/post_log.jsonl

{"post_id":"1789","ts":"2025-10-26T08:12:00Z","topic":"Binflow","format":"one_liner","len":47,"has_media":false,"eng":{"views":110,"likes":2,"replies":0,"quotes":0}}
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models/topic_graph.json

{
  "clusters":[
    {"topic":"Binflow","keywords":["time-labeled","flows","patterns"],"score":0.92},
    {"topic":"MindsEye","keywords":["flowwright","canvas","nodes"],"score":0.88}
  ],
  "motifs":[["Binflow","Temporal AI"],["MindsEye","Flow Economy"]]
}
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composer/brief.yaml

topic: MindsEye
formats: [one_liner, contrast, question, thread]
daily_windows: [09:00, 13:00, 20:00]
media_slots_every: 5
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4) Grok prompt templates (drop-in)

A. Topic clustering

You are GROK-STRUCT. Cluster these posts by topic and motif. 
Return JSON with clusters [{topic, keywords[], score}], and motifs [[topicA, topicB]...].
Posts:
{{PAST_POSTS_TEXT}}
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B. Post composer

You are GROK-STRUCT Composer. 
Generate 12 posts for topic="{{TOPIC}}", targeting formats={{FORMATS}}.
Rules:
- 40–60 words max for one_liner/contrast/question.
- 6–8 tweets for thread with a hook, 4 insights, 1 CTA.
- Add {#AI #Binflow #MindsEye} when relevant.
Return JSON: [{format, text, media_slot:boolean}].
Style: sharp, forward, credible.
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C. Feedback learner

You are GROK-STRUCT Analyst.
Given engagement logs (views/likes/replies/quotes by post), 
output new weights per topic and format (0..1) and 3 recommendations.
Return JSON: {topic_weights, format_weights, recs[]}.
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5) Posting formats (teach the algo)

  • One-liner hook
    “Data doesn’t sit. It rehearses. Build for motion, not storage. #Binflow”

  • Contrast
    “Databases store facts. Flow systems store becoming. That’s why MindsEye wins.”

  • Question
    “What would you build if your data could remember how it moved?”

  • Mini-thread (6–8)

  1. Hook, 2–5) Insights (use the 5 frameworks), 6) CTA: “Full explainer on DEV.”
  • Show-me (visual slot) “Watch a pattern evolve → Sora clip (Flowchart #6).”

6) Algorithmic structure (how it learns you)

  • Topic cadence: each day repeats 3 core topics + 2 rotating.
  • Format cadence: 50% one-liners, 20% contrasts, 20% questions, 10% threads.
  • Reply-first: 10–15 insightful replies on niche accounts before your first batch.
  • A/B tests: same idea, 2 wordings (morning vs evening).

7) Scheduling windows (Africa/Gaborone)

  • Morning: 09:00–10:30 (kickoff + replies)
  • Midday: 13:00–14:00 (thread + media)
  • Evening: 20:00–21:30 (questions to spark replies)

Every 5th post = media slot (Sora chart/flow video).

8) Guardrails (non-negotiable)

  • Respect X rate limits + automation policies.
  • Randomize intervals (±2–6 min) to avoid bot patterns.
  • Rotate links, don’t reuse identical copy in <72h.
  • Keep engagement pods small & organic (no spammy tagging).

9) Minimal build (Python pseudo/real) 

# pip install pydantic schedule httpx
from pydantic import BaseModel
import httpx, schedule, time, json, random, datetime as dt

class Post(BaseModel):
    text: str
    format: str
    topic: str
    media_id: str | None = None

def fetch_metrics():
    # call X API to pull recent post metrics -> store events/post_log.jsonl
    pass

def grok_compose(topic, formats):
    prompt = f"...{topic} {formats}..."
    # call Grok endpoint -> parse JSON
    return [Post(**p) for p in json.loads(GROK_RESPONSE)]

def enqueue(posts):
    for p in posts:
        when = next_slot()
        QUEUE.append((when, p))

def publish(post: Post):
    # X API: create post; attach media if media_id
    print("POST:", post.text)

def loop():
    now = dt.datetime.now()
    for item in list(QUEUE):
        when, post = item
        if when <= now:
            publish(post)
            QUEUE.remove(item)

schedule.every(5).minutes.do(loop)
while True:
    schedule.run_pending()
    time.sleep(1)
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(swap in your X API + Grok client; keep queue persistent with SQLite/Redis).

10) Dashboards (what you track)

  • Topic weight over time (stacked area).
  • Format win-rate (views → replies ratio).
  • Thread retention (avg replies in thread step 3 vs step 6).
  • Media lift (media vs no-media delta).

KPI to watch: profile visits / day and replies per post (leading indicators).

11) Team scaling (who builds this)

  • 1 × System Architect (pipelines + APIs)
  • 1 × Prompt Engineer (Grok briefs, evals)
  • 1 × Data/ML Eng (feedback loop, metrics)
  • 1 × Frontend (dashboard + queue UI)
  • 1 × Content Ops (Sora media + link hygiene)

Small crew (5) can ship v1 in a week.

12) Deploys (pick your stack)

  • API & Scheduler: FastAPI + Celery/Redis on Fly.io or Railway
  • Grok calls: xAI Grok API via server-side only
  • DB: Postgres (topics, posts, metrics)
  • Cache/Queue: Redis
  • Dashboard: Next.js + shadcn/ui
  • Infra alt: all-in on AWS (Lambda + SQS + RDS) or GCP (Cloud Run + Pub/Sub + Cloud SQL)

13) Quick wins you’ll feel in 48h

  • Posts stop feeling “random”; themes recur intentionally.
  • Threads start grabbing replies; questions get answered.
  • Media posts lift CTR.
  • The algo’s topic graph locks onto you.

14) Your first automation (copy this brief to Grok) 

System: You are GROK-STRUCT, an X posting strategist.
User: Build a 24h publishing plan for 3 core topics (Binflow, MindsEye, Temporal AI), 
formats [one_liner, contrast, question, thread], windows [09:00, 13:00, 20:00], 
media_slots_every=5. Return JSON schedule with {time, topic, format, text, media_slot}.
Keep all texts 25–60 words except thread (6–8 steps). Tone: sharp, credible, forward-looking.
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15) Roadmap

  • v1: Composer + Scheduler + Feedback
  • v1.5: Visual recommender (Sora slotting)
  • v2: Cross-platform (DEV/Substack) + auto-threading
  • v3: Audience graph → proactive reply targeting

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