Picture this: It’s 2 AM.

You’re debugging a reactive expression in your Shiny app that worked perfectly yesterday. ChatGPT gives you wrong code. The official documentation is technically correct but doesn’t address your specific edge case. Sound familiar?

After teaching R and Shiny for over a decade, I’ve seen this scenario play out thousands of times. The learning curve isn’t just steep — it’s a cliff face covered in reactive expressions, non-standard evaluation, and cryptic error messages.

That’s why we built something different: an R Shiny AI tutor that doesn’t just regurgitate documentation. It understands R the way experienced developers do.

Beyond Generic AI: The Power of Domain-Specific RAG

Most AI assistants treat R like any other programming language. They’ll happily suggest numpy solutions to your dplyr problems or confidently hallucinate functions that don't exist. We took a radically different approach.

Our RAG (Retrieval-Augmented Generation) model is trained exclusively on:

• thousands of production R scripts from real-world applications
• Every CRAN package documentation (yes, all 20,000+ packages)
• Curated educational content from Hadley Wickham, Jenny Bryan, and other R luminaries
• Battle-tested Shiny applications from Fortune 500 deployments

The result? An AI that thinks in pipes %>%, understands why aes_string() was deprecated, and knows the difference between observe() and observeEvent() without mixing them up.

The Secret Sauce: Zero Hallucination Architecture

Here’s our dirty little secret: Our AI can say “I don’t know.”

Instead of creatively inventing functions, our system:

1. Searches its verified knowledge base first
2. Validates code against actual R syntax
3. Tests reactive logic for consistency
4. Admits uncertainty when beyond its training

Every piece of code is traceable to a source — whether it’s official Posit documentation, a validated Stack Overflow solution, or our proprietary script collection.

Real Learning, Not Just Answers

Traditional documentation tells you what. Stack Overflow tells you how. Our AI tutor tells you why.

Example interaction:

User: "My reactive isn't updating when the file uploads"

The AI doesn’t just fix your code — it teaches you to think in reactive patterns.

What Makes This Different

1. Context-Aware Teaching

The AI remembers your skill level, adapts explanations, and progressively challenges you with harder concepts.

2. Production-Ready Patterns

Learn the patterns actually used in production, not toy examples. Every tutorial includes error handling, performance considerations, and deployment notes.

3. Interactive Debugging

Upload your broken code. The AI diagnoses issues, explains the root cause, and guides you through the fix — teaching principles, not patches.

4. Package Ecosystem Mastery

It knows which packages play well together, which functions are deprecated, and what alternatives exist. No more conflicting dependencies or outdated tutorials.

The Technical Architecture

For the curious, here’s what’s under the hood:

Knowledge Base:

• 2.3 TB of deduplicated R code
• 50,000+ validated script patterns
• Complete CRAN + Bioconductor documentation
• Custom embeddings for R-specific concepts

Validation Layer:

• Syntax checking via R’s native parser
• Reactive flow analysis for Shiny
• Package compatibility verification
• Performance profiling for suggested solutions

Learning System:

• Adaptive difficulty based on user interactions
• Mistake pattern recognition
• Personalized learning path generation
• Progress tracking across concepts

Early Access: 10 Beta Spots Available

We’re opening our beta to just 10 developers. Why so few? Because we want deep, meaningful feedback from serious R users who will push our system to its limits.

Beta testers receive:

• Lifetime premium access (worth $119/month)
• Direct input on feature development
• Custom training on your codebase

We’re looking for:

• Active R/Shiny developers (any level)
• 5–10 hours per week for testing
• Detailed feedback on learning experience
• Passion for improving R education

The Future of R Learning

Imagine an AI that:

• Reviews your pull requests for R best practices
• Automatically optimizes your Shiny app’s performance
• Generates test cases for your functions
• Teaches you new packages as they’re released

This isn’t science fiction. It’s what we’re building.

Join the R-evolution

The R community has always been about democratizing data science. We’re taking that mission further — democratizing expertise itself.

If you’re tired of:

• Generic AI that doesn’t understand R’s quirks
• Outdated tutorials that break with new package versions
• The gap between documentation and real-world usage
• Learning alone without expert guidance

Then you’re exactly who we built this for.

Applications close in 72 hours. 10 spots.

Apply via my email to info@antonioblago.com

Ready to accelerate your R journey?

P.S. — Yes, it knows the difference between <- and =. And yes, it has opinions about which you should use (but it's configurable to your style guide).

P.P.S. — For the skeptics: We’ll be publishing our benchmark results against GPT-4 and Claude on R-specific tasks next month. Spoiler: Domain-specific training matters more than model size.

R-evolution: How We Built an AI Tutor That Actually Understands R and Shiny was originally published in R-evolution on Medium, where people are continuing the conversation by highlighting and responding to this story.

As an SEO analyst working with massive keyword datasets, I kept running into the same wall: the Google Keyword Planner simply couldn’t keep up. Ahrefs or Semrush is way to expensive. Whether it was bulk limits, poor export options, or the lack of automation — it just wasn’t made for serious scale.

So I built my own solution.

Using Python and the DataForSEO API, I created a workflow that can handle 130,000+ keywords, filter them, track API costs, and export clean, structured Excel files — all in a matter of hours.

In this guide, I’ll walk you through how to recreate that workflow from scratch — including:

What tools you’ll need

• How batching and tracking work
• How to structure your project
• A side-by-side comparison with Google’s tool

Let’s get started.

Why Seek an Alternative to the Keyword Planner?

While the Google Keyword Planner is a solid tool, it has notable constraints:

1. Limited Capacity: Struggles with processing extensive keyword lists.
2. Automation Challenges: Lacks flexibility for automated workflows.
3. API Access Restrictions: Requires an active Google Ads account for API usage.

For handling substantial keyword datasets from sources like BigQuery, SEO tools, or proprietary data, a scalable solution like DataForSEO's API + Python proves invaluable.

Getting Started

1. Install Necessary Python Packages

pip install pandas requests openpyxl

2. Set Up DataForSEO Access

Sign up at DataForSEO to obtain your API credentials (login and password).

These credentials are used for Basic Authentication.

Workflow Overview

1. Load keywords from an Excel file.
2. Send them in batches (e.g., 1,000 keywords per batch) to the DataForSEO API.
3. Retrieve data like CPC, search volume, and monthly trends.
4. Track progress, manage costs, and export results.

Implement recovery functions to resume after interruptions.

Key Features of the Python Script

• Batch Processing: Handles large keyword sets and adheres to API rate limits.
• Pickle-Based Progress Tracking: Saves your state so you can pause/resume processing.
• API Cost Monitoring: Tracks costs per batch and generates a report in Excel.
• Keyword Filtering: Cleans up terms with symbols (™ © ®) and limits keyword length.

Suggested Project Structure

project/
│
├── keywords.xlsx            # ✅ Your raw keyword list (must contain a "keyword" column)
│
├── get_sv_data4seo.py       # 🧠 Main script to fetch search volume data via DataForSEO API
│                            # Includes keyword cleaning, batching, retries, and saving
│
├── backup_results.pkl       # 💾 Auto-saved backup with progress & results (resume support)
│                            # Contains: results, monthly data, cost tracking, progress index
│
├── final_results.xlsx       # 📊 Final export with 3 sheets:
│                            #   - "Keyword Data" → search volume, CPC, competition
│                            #   - "Monthly Volume" → time series search volumes
│                            #   - "API Costs" → costs per batch + total
│
├── invalid_keywords.txt     # 🚫 Log of invalid keywords (too long, too many words, bad chars)
│
└── progress.txt             # ⏳ (Optional) Tracks last processed batch for crash recovery

Script Overview

This script automates the collection of keyword data (search volume, CPC, competition, monthly trends) from the DataForSEO API. It reads keywords from an Excel file, cleans them, sends them in batches, handles errors and retries, and saves everything to Excel and Pickle for future use or resume.

1. Setup & Configuration

Import libraries

import os, time, json, base64, pandas as pd, requests, pickle
import numpy as np, re, unicodedata
from dotenv import load_dotenv
load_dotenv()

API Config & Constants

BATCH_SIZE = 1000
LOCATION_CODE = 2276
LANGUAGE_CODE = "de"
MAX_RETRIES = 3
RETRY_DELAY = 3

API_URL = "https://api.dataforseo.com/v3/keywords_data/google_ads/search_volume/live"
AUTH_HEADER = os.getenv("d4seo")

Excel path

def return_path():
    return r"C:\Path\to\your\KeywordList.xlsx"

2. Keyword Cleaning & Validation

Basic cleaning: Remove symbols like ™, ©, etc.

def clean_keyword_simple(kw: str) -> str:
    FORBIDDEN_CHARS = set("™©®✓•→←≠∞¿¡§¶…")
    return ''.join(c for c in kw if c not in FORBIDDEN_CHARS).strip()

Full validation

def is_valid_keyword(kw: str) -> bool:
    if not kw or len(kw.split()) > 10 or len(kw) > 80:
        return False
    # Only allow readable characters
    valid_chars = re.findall(r"[a-zA-Z0-9äöüßéèêáàâíìîóòôúùûčšžăîâăëç\- ]", kw.lower())
    ratio = len(valid_chars) / max(len(kw), 1)
    return ratio >= 0.7 and len(re.findall(r"[a-zA-Z]", kw)) >= 3

Filtering + logging invalid keywords

def filter_keywords(keywords, log_file="invalid_keywords.txt"):
    valid = []
    invalid = []
    for kw in keywords:
        kw_clean = clean_keyword_simple(kw).lower()
        if is_valid_keyword(kw_clean):
            valid.append(kw_clean)
        else:
            invalid.append(kw)
    if invalid:
        with open(log_file, "w", encoding="utf-8") as f:
            f.write("\n".join(invalid))
    return valid

3. Load Keywords and Create Batches

df = pd.read_excel(return_path())
keywords = df["keyword"].dropna().astype(str).tolist()
keywords = filter_keywords(keywords)
keyword_batches = [keywords[i:i + BATCH_SIZE] for i in range(0, len(keywords), BATCH_SIZE)]

4. Send Requests to DataForSEO

Payload creation and request sending

def send_batch_request(keywords_batch):
    payload = [{
        "keywords": keywords_batch,
        "location_code": LOCATION_CODE,
        "language_code": LANGUAGE_CODE,
        "sort_by": "relevance",
        "date_from": "2021-06-01",
        "search_partners": True,
        "include_adult_keywords": False
    }]
    headers = {
        'Authorization': f'Basic {AUTH_HEADER}',
        'Content-Type': 'application/json'
    }
    response = requests.post(API_URL, headers=headers, data=json.dumps(payload))
    return response.json() if response.status_code == 200 else {}

5. Parse and Save Results

def parse_response(response_json):
    keyword_rows, monthly_rows = [], []
    for task in response_json.get("tasks", []):
        for item in task.get("result", []):
            keyword_rows.append({
                "keyword": item.get("keyword"),
                "search_volume": item.get("search_volume"),
                "cpc": item.get("cpc"),
                "competition": item.get("competition")
            })
            for ms in item.get("monthly_searches", []):
                monthly_rows.append({
                    "keyword": item["keyword"],
                    "year": ms["year"],
                    "month": ms["month"],
                    "search_volume": ms["search_volume"]
                })
    return pd.DataFrame(keyword_rows), pd.DataFrame(monthly_rows)

6. Loop With Retry + Resume

for idx in range(start_index, len(keyword_batches)):
    for attempt in range(1, MAX_RETRIES + 1):
        try:
            response = send_batch_request(keyword_batches[idx])
            df_keywords, df_monthly = parse_response(response)
            # Append to existing data and save
            break
        except Exception as e:
            time.sleep(RETRY_DELAY)

7. Save Progress & Results

def save_all_to_pickle(results_df, monthly_df, api_costs_df, progress_index, total_api_cost):
    data = {
        "results": results_df,
        "monthly": monthly_df,
        "api_costs": api_costs_df,
        "status": {
            "progress_index": progress_index,
            "total_cost": total_api_cost
        }
    }
    pd.to_pickle(data, PICKLE_BACKUP)

with pd.ExcelWriter("final_results.xlsx") as writer:
    results_df.to_excel(writer, sheet_name="Keyword Data", index=False)
    monthly_df.to_excel(writer, sheet_name="Monthly Volume", index=False)
    api_costs_df.to_excel(writer, sheet_name="API Costs", index=False)

Bonus: Update Short Time Series Only

def update_short_time_series_keywords(cache_path="traffic_cache.pkl"):
    with open(cache_path, "rb") as f:
        df_all = pickle.load(f)
    short_keywords = [kw for kw in df_all.columns if df_all[kw].dropna().shape[0] <= 12]
    response_json = send_batch_request(short_keywords)
    _, df_monthly = parse_response(response_json)
    # Merge updated values back

✅ Summary

Component Purpose filter_keywords() Cleans and validates input keywords send_batch_request() Sends keyword batches to DataForSEO API parse_response() Extracts structured data from API response save_all_to_pickle() Saves current progress for resume after crash update_short_time_series_keywords() Optionally fetches full historical volume for incomplete entries final_results.xlsx Your final output with keyword metrics, monthly volumes, and API costs

To the full github repo:

GitHub - AntonioBlago/python_seo_tasks: A repo with python scripts, useful for SEO tasks. Star the repo and follow me.

Conclusion

By combining DataForSEO’s API with Python, you create a powerful, flexible, and scalable keyword research system—especially for large-scale analysis. This setup is ideal for SEO professionals and analysts looking for automation and transparency without breaking the bank.

For detailed instructions, code examples, and best practices, check out the original article in German:
Günstigere Alternative zum Keyword Planner

Thank you for being a part of the community

Before you go:

• Be sure to clap and follow the writer ️👏️️
• Follow us: X | LinkedIn | YouTube | Newsletter | Podcast | Twitch
• Start your own free AI-powered blog on Differ 🚀
• Join our content creators community on Discord 🧑🏻‍💻
• For more content, visit plainenglish.io + stackademic.com

Tired of Keyword Planner Limitations? was originally published in Python in Plain English on Medium, where people are continuing the conversation by highlighting and responding to this story.

If you’ve ever opened 10 browser tabs to inspect titles, meta descriptions, and headings — then pasted keyword usage into spreadsheets just to compare who’s ranking and why — you know what I mean.

That’s exactly why I built an Apify (Check out their webscaper) Actor that automates this entire process. But before we jump into how it works, let’s get clear on what a proper keyword and competition analysis actually requires.

Before You Automate: What You Need for a Solid Keyword and Competitor Analysis

SEO success begins with clarity.
If you're not sure which keywords to target or why your competitors are ranking, no tool will save you.

1. Identify the Right Keywords

It’s not just about what gets searched. It’s about why it gets searched and how users behave afterward.

Key factors:

• Search Intent: Is the user looking to buy, learn, or compare?
• Keyword Types: Head terms, long-tails, semantic variations
• SERP Layout: Does Google show shopping ads, AI Overviews, videos?

Example:
“silver rings” vs. “925 sterling silver women’s ring” — one is broad and vague, the other commercial and specific. Your strategy depends on recognizing that.

2. Understand the Competition

Ranking is relative.
To outrank someone, you need to know what they're doing — and do it better.

Here’s what matters:

• Title & Meta Tags: Are they optimized for click-through?
• Headings (H1–H3): Do they align with intent?
• Keyword Usage: Is it natural or stuffed?
• Content Length: Are they ranking with 300 or 3,000 words?
• Entity & Topic Coverage: Do they cover the full context?

This is the tedious part — and the most important.
Until now, this meant hours of manual work.

Enter the SEO On-Page Keyword Analyzer (Apify Actor)

This tool automates exactly what I described — all in a single run.

Here’s how it works:

Step 1: Input One or Multiple Keywords

You can feed in a single keyword or a whole list. The actor runs a live Google search (configurable by country and language) and fetches the top-ranking URLs.

Step 2: Scrape Top Competitors

For each result, the tool extracts:

• Title & Meta Description
• H1, H2, H3 headings
• Word Count
• Keyword usage
• Internal & outbound link count
• TF-IDF and semantic term analysis
• Entity detection (using NLP)

Step 3: Analyze & Compare

It then:

1. Compares your URL (if provided) to top results
2. Highlights missing keywords or weak headings
3. Scores your page on content relevance and depth
4. Flags overuse or keyword stuffing
5. Detects search intent mismatches

You get structured data, CSV reports, and JSON output — perfect for reporting or feeding into a custom SEO dashboard.

Real Use Cases

Here’s where the tool shines:

• Auditing a product or category page
• Optimizing blog articles before publishing
• Launching a new landing page and checking alignment
• Reverse-engineering top competitors' strategies

⚡ Start Now — It’s Live on Apify

👉 Run the SEO On-Page Keyword Analyzer

You can use the actor in n8n, make or zapier automations, schedule runs, connect it to Google Sheets or Notion — or just export and go.
It’s like SurferSEO meets Screaming Frog — but lean, fast, and tailored for automation workflows.

What’s Next

The roadmap includes:

• AI-generated content suggestions
• Competitor change tracking over time
• Integration with GSC & Analytics for performance feedback

If you’re serious about scaling your SEO with systems — and want more than generic keyword tools — this actor is built for you.

Connect with me on LinkedIn, let me know your thoughts.

Continue reading similar aritcles:

• 3 Simple Ways to Check the HTTP Status of Any URL (Without Losing Your Mind)
• A python solution to convert markdown formatting in make or n8n automations
• Maximizing SEO with Internal Linking: A Guide to Visualization and Analysis with this tool & R

We’ve all been there.

You click a link, and… nothing.

A 404.

Or worse, a sneaky redirect to somewhere unexpected.

Whether you're doing technical SEO, maintaining a content-heavy site, or auditing client domains, knowing how to quickly and reliably check the HTTP status of a URL is essential.

Let me show you three practical methods—ranging from dead simple to programmatic. No fluff, just solid ways to get your status codes right.

1. Method: Use Your Browser’s Dev Tools

The easiest way to check a URL's status code is right inside your browser.

Here’s how:

Open the site in Chrome or Firefox.

Right-click → “Inspect”.

Navigate to the Network tab.

Refresh the page.

Click the top request (usually the page URL).

Look for “Status Code” in the Headers tab.

Best for: Quick manual checks
Not great for: Bulk analysis or redirects that resolve before you can catch them

2. Method: Use Command Line with curl

For those comfortable with a terminal, curl is a powerful tool.

Try this:

curl -I https://example.com 

This returns just the headers. You’ll see something like:

HTTP/1.1 301 Moved Permanently Location: https://www.example.com/ 

Add -L to follow redirects:

curl -IL https://example.com 

Best for: Redirect chains, automation, scripting
Not great for: Non-technical users or bulk checks

3. Method 3: Use a Cloud-Based URL Status Checker

Sometimes, you need to go big—like checking 1,000+ URLs. That’s where browser-based methods and curl break down.

It is relevant for online shops or websites where it is critical when your page is not online for a day.

Imagine, you have a product, which makes 1 k $ a day. You loose 1 k a day, when this site is not reachable.

That’s why I created as an E-commerce SEO this actor.

Instead, let a scraper or cloud tool do the work for you.

⚡ My tool: Apify HTTP Status Checker

I built an Apify actor that lets you upload a list of URLs and get back a structured report with:

• HTTP status codes
• Final resolved URLs
• Redirect chains
• Broken or timeout URLs
• Export as Excel or JSON

It runs in the cloud, so no setup needed. Just plug & go.

You can also use it in n8n or make automations. Here is the blog article in German.

https://antonioblago.de/automation/status-checker-workflow-mit-n8n-und-apify-eine-schritt-fuer-schritt-anleitung/

For n8n

{
  "name": "HTTP Status Checker",
  "nodes": [
    {
      "parameters": {
        "url": "https://api.apify.com/v2/acts/antonio_espresso~website-status-code-crawler/run-sync-get-dataset-items",
        "sendQuery": true,
        "queryParameters": {
          "parameters": [
            {
              "name": "token",
              "value": ""
            }
          ]
        },
        "sendHeaders": true,
        "headerParameters": {
          "parameters": [
            {
              "name": "Content-Type",
              "value": "application/json"
            }
          ]
        },
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "={\n  \"start_urls\": [\n    {\n      \"url\": \"{{ $json.url }}\" \n    }\n  ],\n  \"max_depth\": 1\n}\n",
        "options": {}
      },
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4.2,
      "position": [
        420,
        0
      ],
      "id": "9eb4e5b4-12df-40e3-99ed-c254101e31c3",
      "name": "HTTP Request",
      "notesInFlow": true
    },
    {
      "parameters": {
        "assignments": {
          "assignments": [
            {
              "id": "83dc4403-6b9e-4e59-94b4-cd8f76a0cc25",
              "name": "url",
              "value": "",
              "type": "string"
            }
          ]
        },
        "options": {}
      },
      "type": "n8n-nodes-base.set",
      "typeVersion": 3.4,
      "position": [
        220,
        0
      ],
      "id": "65fcae99-1fd4-4bce-8b9b-516024b516dd",
      "name": "Edit Fields"
    },
    {
      "parameters": {
        "rule": {
          "interval": [
            {
              "triggerAtHour": 1
            }
          ]
        }
      },
      "type": "n8n-nodes-base.scheduleTrigger",
      "typeVersion": 1.2,
      "position": [
        0,
        0
      ],
      "id": "bbc97cd7-36ff-460c-bc5d-0efc90ad4aae",
      "name": "Schedule Trigger"
    },
    {
      "parameters": {
        "chatId": "1225059959",
        "text": "={{ $json.message }}",
        "additionalFields": {}
      },
      "type": "n8n-nodes-base.telegram",
      "typeVersion": 1.2,
      "position": [
        840,
        0
      ],
      "id": "a9c3f06b-112d-4bc0-922d-a4be23feeeca",
      "name": "Telegram",
      "credentials": {
        "telegramApi": {
          "id": "",
          "name": "Telegram account"
        }
      }
    },
    {
      "parameters": {
        "jsCode": "// Extract data from input\nconst inputData = $input.first().json;\n\n// Start the message\nlet message = `🚀 *Website Status Code Report* 📊\\n\\n`;\n\n// Add details section\nmessage += `🔍 *Checked URLs:* \\n`;\ninputData.details.forEach(item => {\n    // Assign ✅ for 200, ❌ for 404, and ⚠️ for other status codes\n    let statusIcon = item.status === 200 ? \"✅\" : item.status === 404 ? \"❌\" : \"⚠️\";\n    message += `${statusIcon} [${item.url}](${item.url}) → *${item.status}*\\n`;\n});\n\n// Add summary section\nmessage += `\\n📊 *Status Code Summary:* \\n`;\ninputData.overview.forEach(item => {\n    let statusIcon = item[\"Status Code\"] === 200 ? \"✅\" : item[\"Status Code\"] === 404 ? \"❌\" : \"⚠️\";\n    message += `${statusIcon} *${item[\"Status Code\"]}:* ${item.Count} occurrences\\n`;\n});\n\n// Final message output\nreturn [{ json: { message } }];\n"
      },
      "type": "n8n-nodes-base.code",
      "typeVersion": 2,
      "position": [
        620,
        0
      ],
      "id": "fb9d2b60-c652-4bb2-93ad-41b75af60577",
      "name": "Code"
    }
  ],
  "pinData": {},
  "connections": {
    "Edit Fields": {
      "main": [
        [
          {
            "node": "HTTP Request",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Schedule Trigger": {
      "main": [
        [
          {
            "node": "Edit Fields",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "HTTP Request": {
      "main": [
        [
          {
            "node": "Code",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Telegram": {
      "main": [
        []
      ]
    },
    "Code": {
      "main": [
        [
          {
            "node": "Telegram",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  },
  "active": true,
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "8abc4e32-bf8e-4f80-89bf-b5c2c8d12060",
  "meta": {
    "templateCredsSetupCompleted": true,
    "instanceId": "8765f91253fa7ac79c96beaedfb54b4af7cb62e52733ce474c26d612f6e3136b"
  },
  "id": "wR9giX3QROdRaPfI",
  "tags": []
}

Perfect for:

• SEO audits
• Redirect mapping
• Broken link detection

Keeping your site squeaky clean

👉 Try it out here:

https://apify.com/antonio_espresso/website-status-code-crawler

Final Thoughts

The web is messy. Links break. Redirects go rogue. But with the right tools, checking HTTP status codes becomes painless.

Whether you’re debugging a landing page or mapping legacy redirects, one of these methods will fit your flow.

And if you need a reliable way to check thousands of URLs without losing your weekend, give my Apify actor a spin.

✍️ If you found this useful, feel free to share or drop feedback. And if you’re curious about SEO automation, follow me here on Medium or connect on LinkedIn.

Let me know if you’d like me to publish it directly to Medium or help optimize your actor’s landing page for better conversions.

1 The Hidden Formatting Gap Between Dev & Social Worlds

Markdown was born in 2004 to make developer docs readable in raw text and on the web. Great for READMEs, terrible for TikTok carousels. When you paste ## or 1. into LinkedIn, the platform strips the semantics—leaving a grey wall.

But social feeds are two‑second first impressions. Eye‑tracking studies from Nielsen show users scan for:

• Visual anchors (emoji, caps, icons)
• Short 40–70‑character lines
• Negative space that lets the eye rest

So even if your insight is gold, poor formatting kills dwell time and the algo buries you.

You ask ChatGPT for a LinkedIn thread or carousel. It replies with pristine Markdown:

## 3 Lessons I Learned Scaling to $1M ARR
1. **Focus** on one distribution channel until it breaks 😤
2. *Talk to users daily* — qualitative beats quantitative in the early days 📞
3. Ignore vanity metrics; chase **retention** not followers 🏃‍♂️

You paste it into LinkedIn.

No bullet icons, no hierarchy, walls of text. Engagement tanks.

Why? Platforms don’t render Markdown, and humans fly past anything that looks dense. The best posts are:

• Icon‑driven (🔹 > ◽️ > •)
• Upper‑case highlights for quick scanning
• Single‑line spacing — no double blanks

Yet rewriting every post is a chore, so most creators skip it and settle for mediocre reach.

2 The 90‑Second Manual Fix (That You’ll Never Do)

1. Strip ## / 1. / - prefixes
2. Replace with emoji bullets
3. Upper‑case bold phrases
4. Italicise emphasis with underscores
5. Convert [links](url) → links (url)
6. Pray you didn’t miss a line

Even with TextExpander snippets this is tedious. Creators post daily — compound that over a year and you’re wasting weeks.

3 One‑Click Conversion With markdown2social

Behind the scenes this Apify Actor parses Markdown, then applies four transformation layers:

1. Structure → Detect headings, lists, blockquotes.
2. Visual hooks → Swap ## → 🔹, numbers → 1️⃣, bullets → •.
3. Emphasis → Turn **bold** into ALL‑CAPS, *italic* into italic.
4. Cleanup → Collapse double blanks, convert [link](url) → link (url).

All in under 80 ms on a free plan.

markdown2social is a tiny Apify Actor that turns any Markdown into social‑optimised text:

• Emoji prefixes for H1–H3 & lists
• Readable bullets •, 1️⃣, 🔹
• Inline bold ➜ CAPS, italics ➜ italics
• Removes double line breaks
• Keeps links: Medium (medium.com)

Example

Input Markdown

## Growth secrets
1. **Ship daily**
2. *Talk to users*
> Great products win.

Output Text

🔹 GROWTH SECRETS
1️⃣ SHIP DAILY
2️⃣ _Talk to users_
“Great products win.”

4 How to Run It — in 3 Lines of Python (plus a no‑code option) — in 3 Lines of Python

Quick-Start “AI Markdown → Social” Automation

Below is a ready-made blueprint that turns any AI-generated Markdown (ChatGPT, Gemini, etc.) into perfectly formatted social-media text — fully automated, no manual copy-pasting.

Components

1. Source

• ChatGPT / Gemini API
• or a Google Sheets row, Webhook, Airtable, etc.

2. Formatter

• Apify Actor: markdown2social

3. Distributor

Buffer, Hootsuite, Publer, Twitter/X API, etc.

We’ll wire them together in Make (Integromat), but the same flow works in Zapier or n8n.

2 Step-by-Step in Make

# Module What it does

1 Webhook / OpenAI Receives raw Markdown from your AI

2 Apify — Run Actor

POST https://api.apify.com/v2/acts/USERNAME~markdown2social/run-sync?token={{APIFY_TOKEN}}

Body:

{ "markdown": "{{1.text}}", "heading_style":"title", "emoji_headings": true, "emoji_lists": true }

3 HTTP — Get OUTPUT

GET https://api.apify.com/v2/key-value-stores/{{2.defaultKeyValueStoreId}}/records/OUTPUT?token={{APIFY_TOKEN}}

4 Buffer / Twitter / LinkedIn

Publishes the returned plain-text post

➡ Total setup time: ~10 minutes.

3 If You Prefer Code

from apify_client import ApifyClient
from openai import OpenAI

ai = OpenAI(api_key="OPENAI_KEY")
client = ApifyClient("APIFY_TOKEN")
prompt = "Write a LinkedIn carousel about deep work in Markdown."
md = ai.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": prompt}]
).choices[0].message.content
run = client.actor("USERNAME/markdown2social").call(
    run_input={
        "markdown": md,
        "heading_style": "title",
        "emoji_headings": True,
        "emoji_lists": True,
    }
)
social = client.key_value_store(run["defaultKeyValueStoreId"]).get_record("OUTPUT")["value"]
# ↳ now push `social` to your scheduling tool (Buffer, API, etc.)
print(social)

4 Why This Works

• Structured → Scannable: Unicode icons & caps make headlines pop.
• Zero Copy-Paste: AI → Actor → Scheduler in one flow.
• Configurable: Toggle emojis, heading styles, list formatting.

Ready to try?

1. Deploy the Actor (free tier is enough).
2. Drop the Make scenario (or Python snippet) in your workflow.
3. Watch every AI draft auto-transform into a high-engagement post.

Stop formatting by hand — let the pipeline do it.

from apify_client import ApifyClient
import os

# 1)  Get your API token from an env variable or .env file
APIFY_TOKEN = os.getenv("APIFY_TOKEN", "YOUR_API_TOKEN")
client = ApifyClient(APIFY_TOKEN)

# 2)  Example: AI-generated SEO post in Markdown
seo_post = """
# 5 Reasons Your Website Still Isn’t Ranking

## 1. **Thin Content**

Google rewards depth, not word count. One killer article beats ten shallow posts.

## 2. **Slow Page Speed**

A 3-second delay costs you 40 % of visitors. Compress images, lazy-load scripts.

## 3. **Weak Internal Links**

Pages without context are orphans. Link them like a roadmap.

## 4. **Missing E-E-A-T Signals**

Show real authors, sources, and expertise or risk dropping off page 1.

## 5. **No Keyword Mapping**

Stop guessing. Match search intent to every URL—then own that topic.

---

**Ready to outrank competitors?**

👉  [Book your free SEO audit](https://antonioblago.de/)  

Or explore the full service line-up on [antonioblago.de](https://antonioblago.de) 🚀
"""

run_input = {
    "markdown": seo_post,
    "heading_style": "title",   # Title-case headings
    "emoji_headings": True,     # 🔹 H2 icons, etc.
    "emoji_lists": True         # 1️⃣ 2️⃣ bullets
}

# 3)  Replace with your own Actor slug: username/markdown2social
ACTOR_SLUG = "username/markdown2social"

run = client.actor(ACTOR_SLUG).call(run_input=run_input)

# 4)  Fetch result text from the default Key-Value Store
store_id = run["defaultKeyValueStoreId"]
output_text = client.key_value_store(store_id).get_record("OUTPUT")["value"]

# 5)  Save locally
with open("seo_social_output.txt", "w", encoding="utf-8") as f:
    f.write(output_text)

print("✅ Saved output to 'seo_social_output.txt'")

Prefer no‑code? Use Make/Integromat → HTTP → Run Actor and pipe the defaultKeyValueStoreId → OUTPUT record into your social scheduler.

👉 Try the Actor for free | 🔗 Visit me on antonioblago.de or follow me on LinkedIn