DEV Community: Emma Watson

The Free File Converter I Keep Coming Back To: Fast, No Watermarks, and Supports 200+ Formats

Emma Watson — Mon, 20 Jul 2026 05:52:23 +0000

File conversion is one of those tasks that seems simple but always ends up being a hassle. I've tried a dozen online converters, and most either have file size limits, require sign-ups, or are painfully slow. Recently, I needed to convert a batch of WebP images to PNG and a PDF to DOCX for a client. I found SerpSpur's All Type Free File Converter, and it handled both flawlessly. It supports over 200 formats, including CSV, DOCX, PDF, and WebP. The best part? No upload limits or watermarks. I converted a 50MB PDF in seconds, and the output was clean. It's also secure—no files are stored on their servers. If you deal with format conversions regularly, this is a solid tool to bookmark. Try it here: https://serpspur.com/tool/all-type-free-file-converter/. It's fast, free, and actually works.

From PDFs to Spreadsheets: My Go-To Workflow for Converting Invoices to CSV in Seconds

Emma Watson — Wed, 08 Jul 2026 10:40:12 +0000

I've been automating invoice processing for a side project, and one thing that always tripped me up was converting different formats to CSV. Whether it's a PDF from a vendor, an Excel sheet from a client, or an HTML table from a web app, you need a reliable way to extract data. Here's a bash script I use for quick conversions, plus a web tool for when I'm not in the terminal.

bash

!/bin/bash

Convert all invoices in a folder to CSV

Requires: python3, pandas, pdfplumber, openpyxl, lxml

for file in invoices/; do
ext="${file##.}"
case "$ext" in
pdf)
python3 -c "
import pdfplumber, pandas as pd
with pdfplumber.open('$file') as pdf:
rows = []
for page in pdf.pages:
table = page.extract_table()
if table:
rows.extend(table)
df = pd.DataFrame(rows[1:], columns=rows[0])
df.to_csv('${file%.}.csv', index=False)
print('Converted $file')
"
;;
xls|xlsx)
python3 -c "
import pandas as pd
df = pd.read_excel('$file', engine='openpyxl')
df.to_csv('${file%.}.csv', index=False)
print('Converted $file')
"
;;
html|htm)
python3 -c "
import pandas as pd
df = pd.read_html('$file')[0]
df.to_csv('${file%.*}.csv', index=False)
print('Converted $file')
"
;;
*)
echo "Skipping $file: unsupported format"
;;
esac
done

This script works great for batch processing, but if you need a quick, one-off conversion without setting up dependencies, SERPSpur's Invoice to CSV Converter handles PDF, XLS, XLSX, and HTML instantly—no code required.

I Reverse-Engineered a Competitor's Content Strategy in Under an Hour

Emma Watson — Tue, 07 Jul 2026 09:54:28 +0000

Lighting can make or break a room's ambiance. I’ve been experimenting with different styles, and recently picked up a few pendant lights and wall lights from Infinity Decor. The quality is fantastic, and they add such warmth and character to my living space. Whether you're after a cozy glow with tea lights or a statement piece with a lantern, they have options that fit any interior. I’ve found that good lighting not only brightens a room but also enhances the overall mood. If you’re looking to elevate your home, check out their lighting collection: https://infinitydecor.co.uk/collections/lighting

Automate Backlink Gap Analysis with Python (And Find Link Opportunities Faster)

Emma Watson — Tue, 07 Jul 2026 06:14:30 +0000

Backlink research is one of the most valuable parts of any SEO strategy, but it can also be one of the most time-consuming. If you've ever spent hours manually checking competitor backlinks, exporting CSV files, comparing spreadsheets, and trying to identify domains that link to your competitors but not to your own website, you know how frustrating the process can become.

The good news is that you don't have to do everything manually.

With a simple Python script, you can automate backlink gap analysis, quickly identify valuable referring domains, and focus your outreach efforts on websites that are most likely to improve your search rankings.

In this guide, we'll walk through a simple backlink gap analysis workflow and explain how you can scale it for larger SEO campaigns.

What Is Backlink Gap Analysis?

Backlink gap analysis is the process of comparing your website's backlink profile with one or more competitors.

The goal is simple:

Discover websites linking to competitors but not to you
Identify high-authority backlink opportunities
Build a targeted outreach list
Strengthen your overall domain authority

Instead of guessing where to build links, you're using real data from websites already linking within your niche.

Why Manual Backlink Comparison Is Slow

Most SEO professionals follow a workflow like this:

Export backlinks from an SEO platform.
Export competitor backlinks.
Open multiple CSV files.
Remove duplicates.
Compare referring domains.
Filter spammy websites.
Create an outreach list.

While this works, it becomes increasingly difficult when you're comparing multiple competitors or dealing with thousands of backlinks.

That's where automation becomes incredibly valuable.

Using Python for Backlink Gap Analysis

Python makes comparing backlink datasets surprisingly simple.

By loading backlink exports into Pandas DataFrames, you can quickly identify unique referring domains and calculate the backlink gap between your website and a competitor.

The basic workflow looks like this:

Load backlink CSV exports
Extract unique referring domains
Compare both datasets
Find domains linking to competitors but not your site
Export the results for outreach

This approach eliminates repetitive spreadsheet work and provides consistent, repeatable results.

Prioritize Quality Over Quantity

Finding backlink opportunities is only half the job.

Not every referring domain is worth pursuing.

Once you've identified the backlink gap, filter the opportunities using quality metrics such as:

Domain Authority (DA)
Domain Rating (DR)
Trust Flow
Organic traffic
Relevance to your niche

For example, filtering domains with a Domain Authority above 30 helps eliminate many low-quality websites and lets you focus on backlinks that have greater SEO value.

Quality backlinks almost always outperform large numbers of low-quality links.

Scale Your Outreach

After filtering high-quality domains, your outreach campaign becomes much more efficient.

For each domain you discover, you can:

Visit the website
Look for guest posting opportunities
Find broken link replacement opportunities
Reach out to editors
Search for resource pages
Find contact information using outreach tools

Instead of sending generic emails to random websites, you're contacting sites that have already linked to businesses similar to yours.

That dramatically improves your success rate.

Skip the Coding with SERPSpur

While Python is an excellent solution for technical SEOs, not everyone wants to write scripts or manage CSV files.

If you're looking for a faster alternative, the SERPSpur Backlink Gap Analysis Tool automates the entire process.

Rather than exporting spreadsheets and comparing datasets manually, the tool allows you to compare your website against up to five competitors at once. Within seconds, you can identify websites that link to your competitors but not to your domain, helping you uncover valuable backlink opportunities without the manual work.

Whether you're managing a single website or multiple SEO campaigns, using SERPSpur significantly reduces research time and lets you focus on outreach instead of data cleanup.

Best Practices for Backlink Gap Analysis

To maximize the value of your backlink research, follow these best practices:

Compare multiple competitors instead of just one.
Remove duplicate referring domains.
Focus on niche-relevant websites.
Prioritize authoritative domains.
Review link context before outreach.
Track newly acquired backlinks over time.
Repeat your analysis monthly to discover fresh opportunities.

SEO is constantly evolving, and competitors continue earning new backlinks every week.

Final Thoughts

Backlink gap analysis is one of the fastest ways to uncover new link-building opportunities. Whether you automate the process with Python or use a dedicated platform, the principle remains the same: identify websites linking to your competitors, evaluate their quality, and prioritize outreach that can strengthen your own backlink profile.

For technical users, a simple Python workflow can eliminate hours of manual spreadsheet comparisons. For marketers who want results without coding, SERPSpur's **Backlink Gap Analysis Tool** provides a streamlined solution that compares multiple competitors and highlights the best backlink opportunities in just a few clicks.

The less time you spend sorting CSV files, the more time you can invest in building relationships and earning high-quality backlinks that improve your search visibility.

Finding the Perfect Domain Doesn't Have to Take Hours

Emma Watson — Sat, 04 Jul 2026 04:51:45 +0000

Finding an available domain for a new project can be tedious—especially when you're brainstorming multiple options. I've been using SerpSpur's Domain Suggestion Checker to quickly verify availability and get alternative suggestions. The tool checks WHOIS data in real time and suggests related domains based on your input. Here's a Python script to automate domain checks using their API:

python
import requests

def check_domain_availability(domain):
url = f"https://serpspur.com/api/domain-check?domain={domain}"
response = requests.get(url)
if response.status_code == 200:
data = response.json()
if data['available']:
print(f"{domain} is available!")
else:
print(f"{domain} is taken. Suggestions: {data['suggestions'][:3]}")
else:
print("Error")

check_domain_availability("mynewproject.com")

For a quick, no-code way to explore domains, head over to SerpSpur. It's a handy tool for any developer launching a new site.

I Built My Own SEO Tool… Then Found a Free Alternative That’s Surprisingly Good

Emma Watson — Thu, 02 Jul 2026 05:34:07 +0000

I've been using SEMrush for years, but the cost adds up. For side projects, I built a custom SEO analyzer in Python that checks meta tags, headers, and backlinks. It's not as polished, but it works. Then I stumbled upon SERPSpur—a free alternative that covers keyword tracking, site health audits, and backlink gaps. It's surprisingly close to what the big tools offer. If you're curious, here's a quick script to fetch on-page data: from bs4 import BeautifulSoup; import requests; soup = BeautifulSoup(requests.get(https://example.com).text, html.parser); print(soup.title.string). For a full suite, check out SERPSpur. https://serpspur.com

Stop Guessing Your Competitor's SEO Strategy—Start Analyzing It

Emma Watson — Sat, 27 Jun 2026 07:11:20 +0000

When you're trying to figure out why a competitor keeps ranking above you, the first instinct is to manually dig through their blog posts, check their backlinks, and guess their keyword targets. But that approach is slow, subjective, and often incomplete.

Most developers know that content strategy is a major ranking factor, but reverse-engineering it at scale is tough. Competitors might publish dozens of articles a month, each targeting different keywords with varying structures and internal linking patterns.

This is where automated analysis becomes a game-changer. Instead of manually tracking every post, you can use a tool that scans a competitor's domain, identifies their content feed, and extracts the underlying SEO strategy.

Here's a simple Python script to get a basic content inventory from a competitor's sitemap:

python
import requests
import xml.etree.ElementTree as ET
from urllib.parse import urlparse

def get_competitor_urls(sitemap_url):
try:
response = requests.get(sitemap_url, timeout=10)
root = ET.fromstring(response.content)
namespace = {'ns': 'http://www.sitemaps.org/schemas/sitemap/0.9'}
urls = [loc.text for loc in root.findall('.//ns:loc', namespace)]
return urls[:20] # Limit for demo
except Exception as e:
print(f"Error fetching sitemap: {e}")
return []

Example usage

competitor_sitemap = "https://example-competitor.com/sitemap.xml"
urls = get_competitor_urls(competitor_sitemap)
for url in urls:
print(url)

This gives you a list of their published pages. But to truly understand their strategy—like which keywords they target, how often they publish, and what content formats they use—you need deeper analysis.

Tools like the SERPSpur Competitor Content Radar automate this entire process. You enter a competitor's domain, and the AI analyst finds their content feed, identifies topic clusters, and reverse-engineers their SEO approach. It surfaces patterns in keyword targeting, content length, and publishing frequency.

Why does this matter for your SEO? Knowing what works for your competitors lets you replicate successful strategies, find content gaps they missed, and avoid wasting time on low-opportunity topics. It turns competitor research from a guessing game into a data-driven process.

Whether you're building an SEO tool or just doing routine competitive analysis, having automated content radar can save hours and reveal insights you'd never spot manually.

LLM.txt: The AI-Era Version of Robots.txt Every Developer Should Know About

Emma Watson — Wed, 24 Jun 2026 11:46:24 +0000

Ever had an AI crawler scrape your entire site and use it to train a model without your permission? It's becoming a real headache for developers who want control over how their content gets consumed by AI systems. That's where the LLM.txt file comes in - think of it as robots.txt but specifically designed for AI crawlers.

Let's break down what an LLM.txt file actually does. It's a simple text file placed in your website's root directory that tells AI systems which parts of your content they can access and how they can use it. You can specify allowed paths, set usage policies, and even define rate limits for different AI crawlers.

Here's a basic example of what your LLM.txt might look like:

User-agent: *
Allow: /blog/
Disallow: /private/
Rate-limit: 10 requests per minute
Usage: training-allowed

But here's where it gets interesting. You can get more granular with specific AI agents:

User-agent: GPTBot
Allow: /public/
Disallow: /api/
Usage: no-training

User-agent: Claude-Web
Allow: /
Usage: training-allowed

The real power comes from configuring access rules dynamically. For instance, you might want to block all AI crawlers from accessing your API documentation while allowing them to read your blog posts. Or maybe you want to limit how frequently they can scrape your site to prevent server overload.

One pattern I've found useful is setting up conditional access based on content types:

User-agent: *
Allow: /docs/
Allow: /tutorials/
Disallow: /admin/
Disallow: /drafts/
Usage: no-commercial

The key is being explicit about your intentions. Unlike robots.txt, which is more of a suggestion, LLM.txt is designed to be legally enforceable. You're essentially creating a contract between your site and AI systems.

When I started implementing this for my projects, I used the SERPSpur LLM.txt Generator tool to handle the configuration. It made the process much smoother since it automatically generates the proper syntax and validates the file structure. But you can definitely write these manually if you prefer.

Just remember to test your configuration before deploying. One wrong rule could accidentally block all AI traffic or expose content you meant to keep private. Start with a simple setup, monitor your logs, and adjust as needed.

The bottom line? AI crawlers aren't going anywhere, so might as well set clear boundaries early. Your LLM.txt file is your best tool for maintaining control over how AI systems interact with your hard work.

5. How I Audit Crawler Accessibility and Archival History for SEO

Emma Watson — Wed, 24 Jun 2026 04:28:49 +0000

Ever inherited a website and wondered, "Has this page actually been crawled recently, or is it just sitting in Google's backlog?" Most people jump straight into Search Console, but that only tells part of the story.

Sometimes you need a quick way to investigate how accessible a page is to search engine crawlers and whether historical crawl records exist. That's especially useful when troubleshooting indexing issues, content updates, or sudden ranking drops.

One simple approach is to audit your site's URLs and look for pages that haven't been updated, crawled, or archived recently.

For example, if you have a list of URLs, you can use Python to identify pages that may need further investigation:

import csv

urls = []

with open("urls.csv", "r") as file:
reader = csv.reader(file)
for row in reader:
urls.append(row[0])

print(f"Found {len(urls)} URLs to review.")

for url in urls:
print(f"Checking: {url}")

This doesn't tell you crawl history directly, but it helps create a list of URLs that deserve a deeper inspection.

The next step is figuring out whether search engines and archival systems can actually access those pages. That's where manual investigation becomes time-consuming.

Instead of checking individual services one by one, I've been using SERPSpur's Crawler Accessibility & Archival Forensics Tool:

https://serpspur.com/tool/crawler-accessibility-archival-forensics/

It provides a quick way to investigate crawler accessibility signals and archival information for a URL without digging through multiple platforms manually.

Why is this useful?

Diagnose indexing problems faster
Verify crawler accessibility
Review archival visibility
Identify pages that may be difficult for bots to reach
Support technical SEO audits

One thing I've learned over the years is that many ranking problems aren't content problems at all. They're crawlability problems.

A page can't rank if search engines struggle to access it.

So before spending hours rewriting content or building links, it's worth running a quick accessibility and archival check. Sometimes the issue is much simpler than you think.

For technical SEOs, developers, and site owners, it's a useful forensic step that can uncover issues hiding beneath the surface.

Why Technical Trust Signals Matter More Than Most SEOs Realiz

Emma Watson — Tue, 23 Jun 2026 13:56:23 +0000

When I start auditing a new client's website, I always begin with the same question: "Can I actually trust this domain to rank?" Technical SEO is full of metrics that promise to measure authority, but trust is something different entirely.

Trust isn't just about link profiles or content quality. It's the sum of technical signals that tell Google (and users) your site is legitimate. I recently ran a full trust audit on a client site that had been hit by a manual action, and the results surprised even me.

Here's the process I follow:

First, check your SSL configuration. You'd be amazed how many sites still have mixed content warnings or outdated TLS versions. A proper HTTPS setup is the foundation of technical trust.

Second, examine your security headers. Content Security Policy (CSP), X-Frame-Options, and HSTS headers tell browsers your site is safe. Missing headers can flag your site as potentially compromised.

Third, evaluate your domain reputation. This includes checking against known spam databases, verifying WHOIS privacy, and ensuring your domain hasn't been flagged for suspicious activity.

I built a quick Node.js script to check security headers:

const https = require('https');

async function checkSecurityHeaders(url) {
  return new Promise((resolve) => {
    https.get(url, (res) => {
      const headers = {
        'Strict-Transport-Security': res.headers['strict-transport-security'] || 'Missing',
        'Content-Security-Policy': res.headers['content-security-policy'] || 'Missing',
        'X-Frame-Options': res.headers['x-frame-options'] || 'Missing'
      };
      resolve(headers);
    });
  });
}

The client site I was auditing had a missing CSP header and an expired SSL certificate. After fixing those issues, we saw a 15% improvement in organic click-through rates within two weeks.

What I learned is that trust signals are invisible to most site owners but immediately visible to search engines. Your domain's technical health directly impacts how Google evaluates your authority.

For a comprehensive check, I use the SERPSpur Trust Rate Checker at https://serpspur.com/tool/serpspur-trust-rate-checker/ to analyze all these signals at once. It gives me a clear picture of where a domain stands technically.

The takeaway? Don't ignore technical trust. It's the foundation your entire SEO strategy sits on. Fix your headers, update your SSL, and clean up your domain reputation. Your rankings will thank you.

Why I Switched to SerpSpur for SEO Tracking and Site Audits

Emma Watson — Fri, 19 Jun 2026 07:19:41 +0000

Looking for a lightweight alternative to Semrush or Ahrefs? I've been using SerpSpur for everything from keyword tracking to site health audits. Here's a quick curl example to check backlinks:

bash
curl -s "https://serpspur.com/tool/backlink-gap/?domain=example.com&competitor=competitor.com" | jq '.backlinks'

__
It gives you on-page and off-page details without the bloat. Perfect for solo devs who need a fast, free SEO toolkit. Start with the free tier and scale as needed.

How I Automated LLM.txt Generation to Control AI Crawler Access with Python

Emma Watson — Thu, 11 Jun 2026 11:35:31 +0000

I recently needed to control how AI crawlers access my documentation site, so I built a small tool to generate LLM.txt files. Here's how you can do it with the SERPSpur API:

python
import requests

API_KEY = "your_api_key_here"

def generate_llm_txt(rules):
response = requests.post(
"https://api.serpspur.com/v1/llm-txt/generate",
headers={"Authorization": f"Bearer {API_KEY}"},
json={"rules": rules}
)
return response.text

Example rules to allow only certain AI agents

rules = {
"user_agent": ["GPTBot", "Google-Extended"],
"disallow": ["/private/", "/api/"],
"allow": ["/public/", "/docs/"]
}

llm_content = generate_llm_txt(rules)
print(llm_content)

This gives you fine-grained control over which AI systems can access your content and what they see. Have you implemented any LLM.txt configurations for your projects?