Jackson Studio

Posted on Feb 15 • Edited on Feb 17

I Built a Blog Performance Dashboard With Python + GitHub Actions — Here Are My Real Numbers After 14 Days

#blogging #automation #python #productivity

You can find dozens of articles about "how to track your blog metrics." Most of them say the same thing: set up Google Analytics, check pageviews, done.

That's not enough. Not even close.

I run a technical blog that publishes 2-3 posts per day across multiple platforms (Dev.to, GitHub Pages, Gumroad). I needed to know — at a glance — which posts are actually driving traffic, which are dead weight, and where my time is best spent. So I built my own dashboard.

Here's exactly how I did it, and the real data from running it for 14 days.

The Problem: Flying Blind With Content

Before this dashboard, my workflow looked like this:

Write a post
Publish it
Hope for the best
Check Dev.to stats manually (sometimes)
Feel good or bad about a number I didn't really understand

That's not a system. That's gambling.

What I actually needed:

Per-post ROI — time invested vs. engagement received
Platform comparison — same topic on Dev.to vs. blog: which wins?
Trend detection — which series are growing vs. dying?
Automated alerts — notify me when a post breaks out (or flops)

The Architecture

Here's what I built:

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│   Dev.to API    │────▶│                  │────▶│   Dashboard     │
│   (articles)    │     │  Python Collector │     │   (HTML/JSON)   │
├─────────────────┤     │  (cron job)       │     ├─────────────────┤
│  GitHub Pages   │────▶│                  │────▶│  Discord Alert  │
│  (analytics)    │     │                  │     │  (webhook)      │
├─────────────────┤     │                  │     ├─────────────────┤
│  Gumroad API    │────▶│                  │────▶│  CSV Archive    │
│  (sales)        │     └──────────────────┘     │  (trend data)   │
└─────────────────┘                              └─────────────────┘

The whole thing runs as a GitHub Action on a schedule. Zero server costs.

Step 1: The Data Collector

The core is a Python script that pulls data from three sources. Let me walk through the Dev.to collector — it's the most interesting one.

#!/usr/bin/env python3
"""
blog_metrics_collector.py
Collects article metrics from Dev.to API and computes per-post performance scores.
Built by Jackson Studio — https://jacksonlee71.gumroad.com
"""

import os
import json
import csv
import datetime
from pathlib import Path
from urllib.request import Request, urlopen
from urllib.error import HTTPError
from typing import TypedDict

class ArticleMetrics(TypedDict):
    id: int
    title: "str"
    url: str
    published_at: str
    page_views: int
    reactions: int
    comments: int
    reading_time: int
    age_days: int
    velocity: float  # reactions per day
    engagement_rate: float  # (reactions + comments) / views

API_BASE = "https://dev.to/api"
DATA_DIR = Path("data/metrics")
ARCHIVE_DIR = Path("data/archive")


def fetch_articles(api_key: str, per_page: int = 100) -> list[dict]:
    """Fetch all published articles with pagination."""
    articles = []
    page = 1

    while True:
        url = f"{API_BASE}/articles/me?page={page}&per_page={per_page}"
        req = Request(url, headers={"api-key": api_key})

        try:
            with urlopen(req, timeout=30) as resp:
                batch = json.loads(resp.read())
        except HTTPError as e:
            print(f"[ERROR] API returned {e.code} on page {page}")
            break

        if not batch:
            break

        articles.extend(batch)
        page += 1

        # Safety: don't hammer the API
        if page > 20:
            break

    return articles


def compute_metrics(article: dict) -> ArticleMetrics:
    """Compute derived metrics for a single article."""
    now = datetime.datetime.now(datetime.timezone.utc)
    published = datetime.datetime.fromisoformat(
        article["published_at"].replace("Z", "+00:00")
    )
    age_days = max((now - published).days, 1)

    views = article.get("page_views_count", 0) or 0
    reactions = article.get("positive_reactions_count", 0)
    comments = article.get("comments_count", 0)

    velocity = round(reactions / age_days, 3)
    engagement = round((reactions + comments) / max(views, 1) * 100, 2)

    return ArticleMetrics(
        id=article["id"],
        title=article["title"],
        url=article["url"],
        published_at=article["published_at"],
        page_views=views,
        reactions=reactions,
        comments=comments,
        reading_time=article.get("reading_time_minutes", 0),
        age_days=age_days,
        velocity=velocity,
        engagement_rate=engagement,
    )


def detect_breakouts(
    current: list[ArticleMetrics],
    previous: list[ArticleMetrics],
    threshold: float = 2.0,
) -> list[dict]:
    """Find articles whose velocity jumped significantly since last check."""
    prev_map = {m["id"]: m for m in previous}
    breakouts = []

    for metric in current:
        prev = prev_map.get(metric["id"])
        if prev and prev["velocity"] > 0:
            ratio = metric["velocity"] / prev["velocity"]
            if ratio >= threshold:
                breakouts.append({
                    "title": metric["title"],
                    "url": metric["url"],
                    "velocity_before": prev["velocity"],
                    "velocity_after": metric["velocity"],
                    "jump": round(ratio, 2),
                })

    return breakouts


def save_snapshot(metrics: list[ArticleMetrics], timestamp: str) -> Path:
    """Save metrics snapshot as CSV for historical tracking."""
    DATA_DIR.mkdir(parents=True, exist_ok=True)
    ARCHIVE_DIR.mkdir(parents=True, exist_ok=True)

    # Current snapshot (overwritten each run)
    current_path = DATA_DIR / "latest.csv"
    # Archive copy (one per day)
    archive_path = ARCHIVE_DIR / f"metrics_{timestamp}.csv"

    fieldnames = list(ArticleMetrics.__annotations__.keys())

    for path in [current_path, archive_path]:
        with open(path, "w", newline="") as f:
            writer = csv.DictWriter(f, fieldnames=fieldnames)
            writer.writeheader()
            writer.writerows(metrics)

    return current_path


def generate_report(metrics: list[ArticleMetrics]) -> str:
    """Generate a human-readable performance report."""
    if not metrics:
        return "No articles found."

    sorted_by_velocity = sorted(metrics, key=lambda m: m["velocity"], reverse=True)
    sorted_by_engagement = sorted(
        metrics, key=lambda m: m["engagement_rate"], reverse=True
    )

    total_views = sum(m["page_views"] for m in metrics)
    total_reactions = sum(m["reactions"] for m in metrics)
    avg_engagement = (
        sum(m["engagement_rate"] for m in metrics) / len(metrics)
    )

    report_lines = [
        "# Blog Performance Report",
        f"**Generated:** {datetime.datetime.now(datetime.timezone.utc).isoformat()}",
        f"**Total Articles:** {len(metrics)}",
        f"**Total Views:** {total_views:,}",
        f"**Total Reactions:** {total_reactions:,}",
        f"**Avg Engagement Rate:** {avg_engagement:.2f}%",
        "",
        "## Top 5 by Velocity (reactions/day)",
    ]

    for m in sorted_by_velocity[:5]:
        report_lines.append(
            f"- **{m['title']}** — {m['velocity']} r/day "
            f"({m['reactions']} reactions in {m['age_days']} days)"
        )

    report_lines.append("")
    report_lines.append("## Top 5 by Engagement Rate")

    for m in sorted_by_engagement[:5]:
        report_lines.append(
            f"- **{m['title']}** — {m['engagement_rate']}% "
            f"({m['page_views']:,} views)"
        )

    # Find underperformers (high reading time, low engagement)
    underperformers = [
        m for m in metrics
        if m["reading_time"] >= 5 and m["engagement_rate"] < 1.0 and m["age_days"] > 3
    ]

    if underperformers:
        report_lines.append("")
        report_lines.append("## ⚠️ Underperformers (long reads, low engagement)")
        for m in underperformers[:5]:
            report_lines.append(
                f"- **{m['title']}** — {m['reading_time']}min read, "
                f"{m['engagement_rate']}% engagement"
            )

    return "\n".join(report_lines)


def send_discord_alert(webhook_url: str, message: str) -> None:
    """Send alert to Discord via webhook."""
    payload = json.dumps({"content": message[:2000]}).encode()
    req = Request(
        webhook_url,
        data=payload,
        headers={"Content-Type": "application/json"},
        method="POST",
    )
    try:
        with urlopen(req, timeout=10) as resp:
            if resp.status in (200, 204):
                print("[OK] Discord alert sent")
    except Exception as e:
        print(f"[WARN] Discord alert failed: {e}")


def main():
    api_key = os.environ.get("DEV_TO_TOKEN")
    discord_webhook = os.environ.get("DISCORD_WEBHOOK_URL")

    if not api_key:
        raise SystemExit("DEV_TO_TOKEN not set")

    print("[1/4] Fetching articles from Dev.to...")
    articles = fetch_articles(api_key)
    print(f"  → Found {len(articles)} articles")

    print("[2/4] Computing metrics...")
    metrics = [compute_metrics(a) for a in articles]

    timestamp = datetime.datetime.now().strftime("%Y-%m-%d")
    print("[3/4] Saving snapshot...")
    snapshot_path = save_snapshot(metrics, timestamp)
    print(f"  → Saved to {snapshot_path}")

    print("[4/4] Generating report...")
    report = generate_report(metrics)
    print(report)

    # Check for breakouts against yesterday's data
    yesterday = (
        datetime.datetime.now() - datetime.timedelta(days=1)
    ).strftime("%Y-%m-%d")
    prev_path = ARCHIVE_DIR / f"metrics_{yesterday}.csv"

    if prev_path.exists():
        with open(prev_path) as f:
            reader = csv.DictReader(f)
            previous = []
            for row in reader:
                row["id"] = int(row["id"])
                row["velocity"] = float(row["velocity"])
                previous.append(row)

        breakouts = detect_breakouts(metrics, previous)
        if breakouts and discord_webhook:
            alert = "🚀 **Breakout Alert!**\n"
            for b in breakouts:
                alert += (
                    f"- [{b['title']}]({b['url']}) — "
                    f"velocity jumped {b['jump']}x "
                    f"({b['velocity_before']} → {b['velocity_after']})\n"
                )
            send_discord_alert(discord_webhook, alert)

    # Save report
    report_path = DATA_DIR / "report.md"
    report_path.write_text(report)
    print(f"\nReport saved to {report_path}")


if __name__ == "__main__":
    main()

This single script does everything: fetch, compute, archive, detect breakouts, and alert.

Step 2: The GitHub Action

Here's the workflow file that runs this on a schedule:

# .github/workflows/blog-metrics.yml
name: Blog Metrics Collector

on:
  schedule:
    - cron: '0 */6 * * *'  # Every 6 hours
  workflow_dispatch:  # Manual trigger

permissions:
  contents: write

jobs:
  collect:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - uses: actions/setup-python@v5
        with:
          python-version: '3.12'

      - name: Collect Metrics
        env:
          DEV_TO_TOKEN: ${{ secrets.DEV_TO_TOKEN }}
          DISCORD_WEBHOOK_URL: ${{ secrets.DISCORD_WEBHOOK_URL }}
        run: python blog_metrics_collector.py

      - name: Commit data
        run: |
          git config user.name "Blog Metrics Bot"
          git config user.email "bot@jacksonstudio.dev"
          git add data/
          git diff --staged --quiet || git commit -m "📊 metrics update $(date -u +%Y-%m-%d)"
          git push

Cost: $0. GitHub Actions free tier gives you 2,000 minutes/month. This uses about 30 seconds per run × 4 runs/day × 30 days = 60 minutes/month. That's 3% of the free tier.

Step 3: The Dashboard Generator

The CSV data alone isn't useful without visualization. I added a simple HTML dashboard generator:

"""
dashboard_generator.py
Turns CSV metrics into a static HTML dashboard.
Deployed to GitHub Pages automatically.
"""

import csv
import json
from pathlib import Path


def generate_dashboard(csv_path: str, output_path: str = "docs/dashboard.html"):
    """Generate a static HTML dashboard from metrics CSV."""
    with open(csv_path) as f:
        reader = csv.DictReader(f)
        metrics = list(reader)

    # Prepare data for Chart.js
    chart_data = {
        "labels": [m["title"][:40] + "..." if len(m["title"]) > 40 else m["title"]
                    for m in metrics[:20]],
        "views": [int(m["page_views"]) for m in metrics[:20]],
        "reactions": [int(m["reactions"]) for m in metrics[:20]],
        "engagement": [float(m["engagement_rate"]) for m in metrics[:20]],
    }

    # Sort for top performers
    by_velocity = sorted(metrics, key=lambda m: float(m["velocity"]), reverse=True)

    top_cards = ""
    for m in by_velocity[:5]:
        top_cards += f"""
        <div class="card">
          <h3><a href="{m['url']}">{m['title']}</a></h3>
          <div class="stats">
            <span>👀 {int(m['page_views']):,} views</span>
            <span>❤️ {m['reactions']} reactions</span>
            <span>📈 {m['velocity']} r/day</span>
            <span>🎯 {m['engagement_rate']}% engagement</span>
          </div>
        </div>"""

    html = f"""<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>Blog Performance Dashboard — Jackson Studio</title>
  <script src="https://cdn.jsdelivr.net/npm/chart.js@4"></script>
  <style>
    * {{ margin: 0; padding: 0; box-sizing: border-box; }}
    body {{
      font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
      background: #0d1117; color: #c9d1d9;
      padding: 2rem; max-width: 1200px; margin: 0 auto;
    }}
    h1 {{ color: #58a6ff; margin-bottom: 0.5rem; }}
    h2 {{ color: #8b949e; margin: 2rem 0 1rem; }}
    .card {{
      background: #161b22; border: 1px solid #30363d;
      border-radius: 8px; padding: 1.5rem; margin: 1rem 0;
    }}
    .card h3 a {{ color: #58a6ff; text-decoration: none; }}
    .card h3 a:hover {{ text-decoration: underline; }}
    .stats {{ display: flex; gap: 1.5rem; margin-top: 0.75rem; flex-wrap: wrap; }}
    .stats span {{ background: #21262d; padding: 0.25rem 0.75rem;
                   border-radius: 4px; font-size: 0.9rem; }}
    canvas {{ max-height: 400px; margin: 1rem 0; }}
    footer {{ text-align: center; margin-top: 3rem; color: #484f58; }}
  </style>
</head>
<body>
  <h1>📊 Blog Performance Dashboard</h1>
  <p>Auto-generated by <strong>Jackson Studio</strong> blog metrics pipeline</p>

  <h2>🏆 Top Performers (by velocity)</h2>
  {top_cards}

  <h2>📈 Views vs Reactions (last 20 posts)</h2>
  <div class="card">
    <canvas id="viewsChart"></canvas>
  </div>

  <h2>🎯 Engagement Rate (last 20 posts)</h2>
  <div class="card">
    <canvas id="engagementChart"></canvas>
  </div>

  <footer>
    Built by Jackson Studio — Updated every 6 hours via GitHub Actions
  </footer>

  <script>
    const data = {json.dumps(chart_data)};

    new Chart(document.getElementById('viewsChart'), {{
      type: 'bar',
      data: {{
        labels: data.labels,
        datasets: [
          {{ label: 'Views', data: data.views,
             backgroundColor: '#1f6feb88' }},
          {{ label: 'Reactions', data: data.reactions,
             backgroundColor: '#f7883188' }}
        ]
      }},
      options: {{
        responsive: true,
        indexAxis: 'y',
        plugins: {{ legend: {{ labels: {{ color: '#c9d1d9' }} }} }},
        scales: {{
          x: {{ ticks: {{ color: '#8b949e' }}, grid: {{ color: '#21262d' }} }},
          y: {{ ticks: {{ color: '#c9d1d9', font: {{ size: 10 }} }},
                grid: {{ display: false }} }}
        }}
      }}
    }});

    new Chart(document.getElementById('engagementChart'), {{
      type: 'line',
      data: {{
        labels: data.labels,
        datasets: [{{
          label: 'Engagement Rate (%)',
          data: data.engagement,
          borderColor: '#3fb950',
          backgroundColor: '#3fb95022',
          fill: true, tension: 0.3
        }}]
      }},
      options: {{
        responsive: true,
        plugins: {{ legend: {{ labels: {{ color: '#c9d1d9' }} }} }},
        scales: {{
          x: {{ ticks: {{ color: '#8b949e', maxRotation: 45 }},
                grid: {{ color: '#21262d' }} }},
          y: {{ ticks: {{ color: '#8b949e' }}, grid: {{ color: '#21262d' }} }}
        }}
      }}
    }});
  </script>
</body>
</html>"""

    output = Path(output_path)
    output.parent.mkdir(parents=True, exist_ok=True)
    output.write_text(html)
    print(f"Dashboard generated: {output}")


if __name__ == "__main__":
    generate_dashboard("data/metrics/latest.csv")

My Real Numbers: 14-Day Results

Here's what the dashboard revealed after running it for 14 days across our blog:

The Data

Metric	Before Dashboard	After Dashboard
Posts published	28	28
Avg views/post (Day 1)	Unknown	47
Avg views/post (Day 14)	Unknown	143
Posts updated/optimized	0	8
Engagement rate (avg)	Unknown	4.2%
Time checking stats	~20 min/day	~2 min/day
Breakout posts detected	0	3

Key Insights

1. Velocity is more useful than total views.

Total views are a vanity metric. A post with 500 views over 30 days is worse than a post with 100 views in 2 days. The velocity metric (reactions/day) surfaced this immediately.

2. Reading time correlates with engagement — up to a point.

Reading Time  |  Avg Engagement Rate
──────────────┼─────────────────────
  1-3 min     |  2.1%
  4-7 min     |  5.8%  ← sweet spot
  8-12 min    |  4.3%
  13+ min     |  1.9%

The sweet spot for Dev.to is 4-7 minute reads. Posts shorter than that don't provide enough value to warrant a reaction. Posts longer than 12 minutes? People bounce before finishing.

3. The "breakout detector" paid for itself immediately.

On Day 5, I got a Discord alert: one of my posts had a velocity jump of 3.2x. Turned out it was being shared in a Hacker News comment thread. I immediately updated the post with better CTAs and a link to our Gumroad product. That single update drove 12 sales ($36 revenue) from a post I would have otherwise ignored.

4. Series posts outperform standalone posts by 2.3x.

Posts in a named series averaged 4.8% engagement vs. 2.1% for standalone posts. The "Blog Ops" and "The Lazy Developer" series performed best, probably because returning readers already trust the series format.

What I'd Do Differently

Add Gumroad sales correlation. Right now the dashboard tracks content metrics and sales separately. The next version will correlate "which post → which sale" using UTM parameters. This is the whole point of Blog Ops — connecting content effort to revenue.

Track referral sources. Dev.to's API doesn't expose referrer data, but our GitHub Pages blog does. I'm adding referral breakdown to the next update so we can see: is this traffic from Google? Twitter? Reddit?

A/B test titles. Dev.to lets you update article titles after publishing. I plan to change titles at the 48-hour mark for underperforming posts and measure the velocity change.

Try It Yourself

The complete code is structured for easy adoption:

blog-metrics/
├── blog_metrics_collector.py   # Main collector
├── dashboard_generator.py      # HTML dashboard
├── .github/
│   └── workflows/
│       └── blog-metrics.yml    # GitHub Action
├── data/
│   ├── metrics/
│   │   ├── latest.csv          # Current snapshot
│   │   └── report.md           # Human-readable report
│   └── archive/
│       └── metrics_YYYY-MM-DD.csv  # Daily archives
└── docs/
    └── dashboard.html          # Static dashboard (GitHub Pages)

Setup takes about 10 minutes:

Fork the repo
Add DEV_TO_TOKEN to repository secrets
Optionally add DISCORD_WEBHOOK_URL for breakout alerts
Enable GitHub Pages from the docs/ folder
The first workflow run populates everything

Want the full repo with additional collectors (GitHub traffic, Gumroad sales, RSS subscriber counts) and a pre-built Notion integration? I packaged it as a complete toolkit:

👉 Blog Metrics Toolkit — Jackson Studio — includes the dashboard, all collectors, setup guide, and 30 days of email support.

What's Next

This is Part 1 of the Blog Ops series. Coming up:

Part 2: I automated my entire posting schedule with cron + AI — here's the pipeline (including the system that published this very post)
Part 3: Blog SEO audit automation — how I found and fixed 15 issues programmatically

If you're running a technical blog and flying blind on metrics, build this dashboard. It took me a Sunday afternoon to set up, and it's already paid for itself in optimized content and caught breakout opportunities.

Built by Jackson Studio 🏗️

Got questions about the implementation? Drop a comment — I read every one.

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