From WordPress Plugin to Chrome Extension: Building TL;DR Content Summarizer – A Journey in AI-Powered Content Curation

By C2IT Labs

Introduction: The Problem That Started It All

In an age where information overload is the norm rather than the exception, the ability to quickly understand and digest content has become a superpower. We’re constantly bombarded with articles, blog posts, research papers, and web pages that demand our attention, but who has the time to read everything in full?

This is the problem that sparked the creation of the TL;DR Content Summarizer—a project that began as a simple WordPress plugin and evolved into a powerful Chrome extension capable of summarizing any webpage on the internet. What started as a weekend project to help WordPress site owners provide quick summaries of their content has grown into a comprehensive solution that works across the entire web.

In this 5000-word deep dive, I’ll take you through the entire journey—from the initial WordPress plugin concept to the sophisticated Chrome extension with site-specific extractors. We’ll explore the technical challenges, design decisions, and the evolution of the project through its git history. Most importantly, I’ll share the fun, the frustrations, and the lessons learned along the way.

Part 1: The WordPress Plugin – A Foundation for AI-Powered Summarization

The Initial Vision

The project began with a simple but powerful idea: what if every WordPress post could automatically generate a “Too Long; Didn’t Read” summary? This wasn’t just about convenience—it was about accessibility, user experience, and helping content creators provide value to their readers in an increasingly attention-scarce world.

The WordPress plugin, committed to git on the initial commit (5b017ac - TLDR plugin Initial commit), was designed to be drop-in simple. The goal was to create something that any WordPress site owner could install, configure with their OpenAI API key, and immediately start using without any technical expertise.

Architecture and Design Philosophy

The WordPress plugin follows a clean, modular architecture that separates concerns beautifully:

Core Components:

class-tldr-admin.php – Handles all admin configuration, settings pages, and API key management
class-tldr-frontend.php – Manages the user-facing button, modal display, and AJAX interactions
class-tldr-openai.php – Encapsulates all OpenAI API communication
class-tldr-content-parser.php – Handles content extraction, heading detection, and response parsing
class-tldr-cache.php – Manages caching to reduce API calls and improve performance

This separation of concerns wasn’t just good practice—it made the codebase maintainable, testable, and extensible. When we later built the Chrome extension, we could reuse many of these concepts, adapting them to the browser environment.

The Content Extraction Challenge

One of the most interesting challenges in building the WordPress plugin was content extraction. WordPress posts can be rendered in countless ways depending on the theme, page builder, and customizations. The plugin needed to handle:

Standard WordPress content – Simple post content from the database
DOM-based extraction – Content that’s been modified by JavaScript, page builders, or themes
Dynamic content – AJAX-loaded content that doesn’t exist in the initial HTML

The solution was elegant: the plugin supports both server-side extraction (from the post content in the database) and client-side extraction (from the rendered DOM). The JavaScript frontend can detect when DOM extraction is needed and send that content to the server via AJAX.

This dual approach ensures the plugin works across the vast ecosystem of WordPress themes and page builders, from simple themes to complex Elementor or Gutenberg setups.

Caching Strategy

Early in development, it became clear that caching was essential. Every time a user requested a summary, we were making an API call to OpenAI, which:

Costs money (API tokens aren’t free)
Takes time (network latency)
Could hit rate limits

The caching system stores summaries per post ID, with timestamps. This means:

First request: Generate summary and cache it
Subsequent requests: Serve from cache instantly
Cache invalidation: Clear cache when posts are updated

The cache implementation uses WordPress transients, which are perfect for this use case—they’re automatically cleaned up by WordPress and can be easily cleared programmatically.

User Experience Features

The WordPress plugin includes several thoughtful UX features:

Display Modes:

Modal – A floating button that opens a modal with the summary
Content Block – Inline summary that appears above or below the post content

Customization Options:

Button placement (bottom-right, bottom-left, etc.)
Button colors and sizes
Custom button text and icons
Post type filtering (only show on posts, pages, or custom post types)

Visual Indicators:

Cache status indicator (shows if summary is cached)
Extraction method indicator (shows if using DOM or post content)

These features make the plugin feel polished and professional, not just a proof of concept.

The OpenAI Integration

Integrating with OpenAI’s API was straightforward but required careful prompt engineering. The plugin sends the extracted content along with detected headings to OpenAI, requesting a summary in a specific format. The response is then parsed to extract:

The summary text
A table of contents (if headings were detected)
Any metadata

The prompt is carefully crafted to ensure consistent, useful summaries regardless of the input content. We experimented with different prompt structures, verboseness levels, and instructions to find what works best across different types of content.

Part 2: The Evolution to Chrome Extension – Taking It Beyond WordPress

The “What If” Moment

After the WordPress plugin was working well, a natural question emerged: “What if this could work on ANY website, not just WordPress?” This was the genesis of the Chrome extension project.

The Chrome extension, first committed as 381cea9 - chrome-extension-initial-commit, represents a significant evolution in scope and capability. While the WordPress plugin is limited to WordPress sites, the Chrome extension can summarize any webpage on the internet.

Technical Challenges of Browser Extension Development

Building a Chrome extension presented unique challenges compared to the WordPress plugin:

1. Manifest V3 Migration Chrome extensions now use Manifest V3, which has significant differences from V2:

Service workers instead of background pages
ES modules support
Different permission models
Side Panel API for modern UI

We had to learn and adapt to these new patterns, which required rethinking how background tasks work.

2. Content Script Injection Unlike WordPress where we control the server, browser extensions need to inject scripts into pages we don’t control. This means:

Handling Content Security Policies
Dealing with iframes and complex page structures
Extracting content from pages with dynamic JavaScript
Programmatic injection fallbacks for sites that block content scripts

3. Cross-Component Communication Chrome extensions have multiple contexts:

Background service worker
Content scripts (run in page context)
Side panel (separate HTML page)
Options page

Coordinating between these contexts requires careful message passing and state management.

The Side Panel Revolution

One of the most exciting features of the Chrome extension is the Side Panel API. Instead of a popup or modal, the extension uses Chrome’s native side panel—a persistent, resizable panel that stays open as you navigate.

This creates a much better user experience:

The summary stays visible while browsing
You can navigate between pages and see summaries update
It feels integrated into Chrome, not like a separate tool

The side panel implementation required learning Chrome’s Side Panel API, which is relatively new. We had to handle:

Opening the panel programmatically
Syncing with page navigation
Managing state across page changes
Handling URL changes and cache updates

Content Extraction in the Wild

Extracting content from arbitrary websites is much harder than extracting from WordPress posts. Every website has a different structure, different CSS classes, different JavaScript frameworks.

The Chrome extension uses a sophisticated content extraction strategy:

1. Generic Extraction First, it tries to find the main content using heuristics:

Look for semantic HTML5 elements (<main>, <article>)
Find the largest text block
Remove navigation, headers, footers, sidebars
Clean up the text (remove extra whitespace, normalize)

2. Programmatic Injection Fallback For sites with strict Content Security Policies (like Google Docs, ChatGPT), the extension falls back to programmatic script injection:

Uses chrome.scripting.executeScript() to inject extraction code
Tries main frame first, then all frames for iframe support
Handles errors gracefully

3. Site-Specific Extractors For complex sites that need custom logic, we built a modular extractor system (more on this later).

Part 3: The Site-Specific Extractor System – Extensibility by Design

The Problem with Generic Extraction

While generic content extraction works for most websites, some sites have unique structures that require custom logic. For example:

LinkedIn profiles – The actual profile content is buried in complex React components
Facebook profiles – Similar issues with dynamic content and complex DOM structures
News sites – Paywalls, subscription prompts, and complex layouts
Documentation sites – Multi-column layouts, code blocks, navigation

Generic extraction often fails on these sites, returning navigation menus, ads, or empty content instead of the actual article.

The Extractor Architecture

The solution was to build a modular, extensible extractor system. Each site-specific extractor is a self-contained module that:

Matches URLs – Uses regex patterns to detect when it should be used
Extracts Content – Has custom logic to find and extract the relevant content
Returns Structured Data – Provides content in a consistent format
Defines Metadata – Includes name, icon, display name, and summary style

The system is designed to be easily extensible. Adding a new extractor is as simple as:

Create a new file in utils/extractors/
Implement the matches() and extract() methods
Register it in site-specific-extractors.js

LinkedIn Extractor – A Case Study

The LinkedIn extractor (linkedin-extractor.js) is a great example of why site-specific extractors are necessary. LinkedIn profiles have:

Name in specific selectors
Headline in another location
About section that’s often collapsed
Experience items in a complex list structure
Education in a separate section
Skills that are dynamically loaded

The LinkedIn extractor navigates this complexity by:

Using specific CSS selectors for each piece of data
Handling collapsed sections (clicking “See more” buttons)
Combining all the data into a coherent profile summary
Using a “formal” summary style (appropriate for professional profiles)

Facebook Extractor – Another Example

The Facebook extractor (facebook-extractor.js) handles Facebook profile pages, which have their own unique challenges:

Profile information in different locations than LinkedIn
Bio text that may be truncated
Work and education in different formats
Places lived information
Privacy settings that may hide content

The Facebook extractor uses a “casual” summary style, which is more appropriate for social media profiles than the formal style used for LinkedIn.

Style Override System

One of the clever features of the extractor system is automatic style override. When a site-specific extractor is used, it can specify a preferred summary style (like “formal” for LinkedIn or “casual” for Facebook). This style automatically overrides the user’s default style setting, ensuring that summaries match the context of the content.

This happens transparently—the user doesn’t need to change settings. The extension detects the site, uses the appropriate extractor, and applies the right style automatically.

The Extensibility Promise

The extractor system is designed to grow. The README in utils/extractors/ provides clear documentation for adding new extractors. The pattern is simple:

Create a class with matches() and extract() methods
Export it
Register it in the main extractor registry

This makes it easy for developers (or even the original creators) to add support for new sites as needed. Want to add a Reddit extractor? A Wikipedia extractor? A GitHub extractor? Just follow the pattern.

Part 4: Advanced Features and Polish

Caching and Performance

Both the WordPress plugin and Chrome extension implement sophisticated caching:

WordPress Plugin:

Caches summaries per post ID
Automatically clears cache when posts are updated
Uses WordPress transients (automatic cleanup)
Shows cache status to users

Chrome Extension:

Caches summaries per URL
Stores cache statistics (count and size)
Allows manual cache reset
Shows cache timestamp and statistics in the footer

The caching system is crucial for:

Cost savings – Reduces API calls
Performance – Instant summary display for cached content
User experience – No waiting for API responses on repeat visits

Summary Styles – Making AI Responses More Useful

One of the most interesting features we added was summary styles. Instead of just one type of summary, users can choose from:

Normal – Standard summary
Technical – Includes technical highlights and details
Formal – Professional, formal language
Funny – Lighthearted with a joke at the end
Quotable – Extracts notable quotes
Concise – Very brief summary
Academic – Scholarly tone
Casual – Informal, conversational
Critical – Analytical and critical perspective
Storytelling – Narrative style

Each style modifies the prompt sent to OpenAI, resulting in summaries that match the desired tone and format. This makes the tool much more versatile—a technical blog post might benefit from a “Technical” style, while a personal blog might work better with “Casual” or “Storytelling.”

Verboseness Levels

In addition to styles, users can control verboseness:

Brief – Very short summaries
Normal – Standard length
Detailed – Comprehensive summaries
Custom – User-defined verboseness instructions

This gives users fine-grained control over the output, ensuring summaries match their needs.

A recent addition to the Chrome extension is social sharing functionality. Users can share summaries to:

Twitter/X – With character limit handling
Facebook – Using Web Share API or clipboard fallback
LinkedIn – Professional sharing
Reddit – Community sharing
Copy to Clipboard – For any platform

The sharing feature includes smart handling of platforms that don’t support pre-filled text (like Facebook and LinkedIn). It copies the summary to clipboard and opens the share dialog, with a helpful message to paste the text.

Visual Design and UX

Both the WordPress plugin and Chrome extension prioritize user experience:

WordPress Plugin:

Customizable button (colors, sizes, placement)
Modal or inline display options
Visual indicators for cache and extraction status
Smooth animations and transitions

Chrome Extension:

Modern side panel interface
Clean, readable summary display
Markdown rendering (converts markdown to HTML)
Footer with cache info, settings, and attribution
Loading states and error handling
Blank state with generate button

The design philosophy is “get out of the way”—the tools should be powerful but unobtrusive, helpful but not distracting.

Part 5: The Development Journey – Git History Tells the Story

Looking at the git history provides insight into the development process:

Initial Commit (`5b017ac - TLDR plugin Initial commit`)

The WordPress plugin started with a complete, working implementation. This wasn’t a “hello world” commit—it was a fully functional plugin with all core features. This suggests the plugin was developed locally first, then committed as a cohesive unit.

Chrome Extension Initial Commit (`381cea9 - chrome-extension-initial-commit`)

The Chrome extension began as a separate project, built on the learnings from the WordPress plugin. The initial commit likely included:

Basic manifest.json
Content script for extraction
Background service worker
Side panel UI
Options page

Icon Development (`cd47569 - icons`)

Icons are often an afterthought, but they’re crucial for user experience. This commit shows attention to detail—creating proper icon assets in multiple sizes (16×16, 48×48, 128×128) for different Chrome UI contexts.

Iterative Improvements

The commits show a pattern of iterative improvement:

52d5dbc - working nice! – Early validation that things are working
f179505 - working great – Further refinement
52a577d - cache summary – Adding caching functionality
843aa8b - tested – Testing and validation
e31d939 - cleanup – Code cleanup and refactoring
4e5e927 - site extractors – Adding the site-specific extractor system

This pattern shows a healthy development process: build, test, refine, add features, clean up, repeat.

The Fun of Building

What makes this project fun? Several things:

1. Solving Real Problems This isn’t a toy project—it solves a real problem that people face daily. The satisfaction of building something useful is immense.

2. Learning New Technologies Building the Chrome extension required learning:

Manifest V3
Chrome Extension APIs
Service workers
Side Panel API
Content script injection
Message passing between contexts

3. The Challenge of Content Extraction Extracting content from arbitrary websites is like solving puzzles. Each site is different, and finding the right selectors and logic is satisfying when it works.

4. Seeing It Work There’s something magical about clicking a button and watching AI generate a summary of a webpage. The first time it worked was a “wow” moment.

5. The Extensibility Building the extractor system was particularly satisfying because it creates a framework for future growth. Adding a new extractor feels like adding a new superpower.

Part 6: Technical Deep Dive – How It All Works

WordPress Plugin Flow

User clicks TLDR button → JavaScript detects click
Check cache → AJAX call to check if summary exists
If cached → Display immediately
If not cached → Extract content (DOM or post content)
Send to OpenAI → API call with content and headings
Parse response → Extract summary and TOC
Cache result → Store for future requests
Display to user → Show in modal or content block

Chrome Extension Flow

User clicks extension icon → Opens side panel
Get current tab URL → Query active tab
Check cache → Look for cached summary for URL
If cached → Display immediately
If not cached → Extract content from page:
- Try content script message
- Fallback to programmatic injection
- Try site-specific extractor
- Fallback to generic extraction
Send to OpenAI → API call with content
Parse response → Convert markdown to HTML
Cache result → Store with URL as key
Display to user → Show in side panel

Content Extraction Algorithm

The generic content extraction uses a multi-step process:

Find main content area:
- Look for <main>, <article>, or content-like classes
- Find the largest text block
- Exclude navigation, headers, footers
Clean content:
- Remove script and style tags
- Remove navigation elements
- Remove ads and promotional content
- Normalize whitespace
Extract headings:
- Find all <h1> through <h6> tags
- Generate anchor IDs
- Create heading hierarchy
Return structured data:
- Clean text content
- Array of headings with IDs
- Metadata about extraction method

OpenAI Integration

The OpenAI integration is straightforward but carefully tuned:

Build prompt:
- Include content to summarize
- Add headings if available
- Include style instructions
- Add verboseness instructions
- Include custom instructions if set
Make API request:
- Use appropriate model (gpt-3.5-turbo or gpt-4)
- Set max tokens based on verboseness
- Set temperature for creativity
- Handle errors and timeouts
Parse response:
- Extract summary text
- Parse markdown formatting
- Extract usage statistics (tokens used)
- Handle errors gracefully

Caching Implementation

WordPress:

Uses WordPress transients
Key: tldr_summary_{post_id}
Stores: Summary, TOC, headings, timestamp
Expiration: Never (manual clearing)

Chrome Extension:

Uses chrome.storage.local
Key: URL (normalized)
Stores: Summary, headings, style, verboseness, usage, extractor info, timestamp
Statistics: Total count and size calculated on demand

Part 7: Challenges and Solutions

Challenge 1: Content Script Injection Failures

Problem: Some sites (Google Docs, ChatGPT) have strict Content Security Policies that prevent content script injection.

Solution: Implemented programmatic injection fallback using chrome.scripting.executeScript(). Tries main frame first, then all frames for iframe support.

Challenge 2: Markdown Not Rendering

Problem: OpenAI returns markdown, but it was displaying as raw markdown text instead of formatted HTML.

Solution: Built a markdownToHTML() function that converts markdown syntax (headers, bold, italic, links, code blocks, lists) to HTML. The order of processing matters—lists must be handled before paragraphs.

Challenge 3: TOC Links Not Navigating

Problem: Table of Contents links were trying to navigate within the side panel instead of scrolling to anchors in the main page.

Solution: Changed TOC items from <a> tags to <button> elements styled to look like links. This prevents browser navigation. Added message passing to content script to handle scrolling and highlighting.

Challenge 4: Summary Generation Hanging

Problem: Summary generation would sometimes hang without showing errors.

Solution:

Added safeSendResponse wrapper with 60-second timeout
Implemented AbortController for fetch requests
Improved error handling to ensure responses are always sent
Fixed duplicate variable declarations

Challenge 5: Facebook/LinkedIn Only Passing URL

Problem: Facebook and LinkedIn share dialogs don’t support pre-filled text via URL parameters.

Solution:

Try Web Share API first (works on mobile and some desktop browsers)
Fallback to copying text to clipboard and opening share dialog
Show visual feedback (checkmark) and alert message

Challenge 6: Site-Specific Extractors Not Applying Style

Problem: When site-specific extractors were used, the style override wasn’t being applied.

Solution:

Detect extractor match early (before content extraction)
Preserve extractor info through all extraction paths
Apply style override in OpenAI client
Ensure extractor info is passed even when extraction fails

Part 8: Future Possibilities and Extensions

Potential Enhancements

1. More Site-Specific Extractors The extractor system is designed to grow. Potential additions:

Reddit posts and comments
Wikipedia articles
GitHub repositories and issues
YouTube video descriptions and comments
Amazon product pages
News articles (with paywall handling)

2. Batch Processing Allow users to summarize multiple pages at once, useful for research or content curation.

3. Summary History Keep a history of all summaries generated, searchable and filterable.

4. Export Options Export summaries to various formats (PDF, Markdown, Word, etc.).

5. Custom Prompts Allow users to define completely custom prompts for specialized use cases.

6. Multi-Language Support Detect page language and generate summaries in that language or translate summaries.

7. Integration with Other Services

Save summaries to Notion, Evernote, etc.
Send summaries via email
Create calendar events from event pages

8. Analytics and Insights Track which pages are summarized most, average summary length, token usage over time, etc.

Technical Improvements

1. Better Content Extraction

Machine learning models to identify main content
Better handling of dynamic content
Support for more content types (PDFs, images with text)

2. Performance Optimization

Lazy loading of extractors
Background pre-fetching of summaries
Better caching strategies

3. Error Recovery

Automatic retry with exponential backoff
Fallback to different models
Offline mode with cached summaries

4. Security Enhancements

Encrypted API key storage
Secure content extraction
Privacy-focused options

Part 9: Lessons Learned

What Went Well

Modular Architecture – Separating concerns made the codebase maintainable and extensible
Caching Strategy – Implementing caching early saved costs and improved UX
Extractor System – Building an extensible system from the start paid off
User Experience Focus – Paying attention to UX details made the tools feel polished
Iterative Development – Building, testing, and refining in cycles worked well

What Could Be Improved

Testing – More automated tests would have caught issues earlier
Documentation – While code is well-commented, user documentation could be expanded
Error Messages – Some error messages could be more user-friendly
Performance – Some optimizations could reduce initial load time
Accessibility – More attention to accessibility features would improve usability

Key Takeaways

Start Simple, Extend Later – The WordPress plugin was a great foundation for the Chrome extension
Design for Extensibility – The extractor system shows the value of building extensible systems
User Experience Matters – Small UX improvements make a big difference
Caching is Critical – For API-based tools, caching is essential for cost and performance
Iterate and Refine – The git history shows the value of iterative improvement

Part 10: Conclusion – The Joy of Building Useful Tools

Building the TL;DR Content Summarizer has been a journey of learning, problem-solving, and creation. From a simple WordPress plugin to a sophisticated Chrome extension with site-specific extractors, the project has grown organically based on real needs and use cases.

What makes this project special isn’t just the technology—it’s the fact that it solves a real problem that people face every day. In a world of information overload, the ability to quickly understand content is valuable. The TL;DR Content Summarizer makes that possible.

The extensibility of the extractor system means the tool can grow to handle more and more sites. The modular architecture means it’s maintainable and extensible. The focus on user experience means it’s a joy to use.

But perhaps the most rewarding aspect has been the process itself—the fun of solving technical challenges, the satisfaction of seeing it work, and the knowledge that this tool can help people consume information more efficiently.

Whether you’re a WordPress site owner looking to provide better summaries for your readers, or a Chrome user who wants to quickly understand any webpage, the TL;DR Content Summarizer is there to help. And with the extensible extractor system, it will only get better over time.

Technical Specifications

WordPress Plugin

Language: PHP 7.4+
Framework: WordPress 5.0+
Dependencies: OpenAI API
Storage: WordPress transients
Frontend: JavaScript (jQuery), CSS

Chrome Extension

Manifest Version: 3
Language: JavaScript (ES6+)
APIs Used:
- Chrome Side Panel API
- Chrome Storage API
- Chrome Scripting API
- Chrome Tabs API
Dependencies: OpenAI API
Storage: chrome.storage.local

OpenAI Integration

Models Supported: gpt-3.5-turbo, gpt-4, gpt-4-turbo
Features:
- Custom prompts
- Style variations
- Verboseness control
- Token usage tracking

Acknowledgments

This project was developed by C2IT Labs. For more information, visit c2itconsulting.net.

The TL;DR Content Summarizer represents the intersection of AI technology and practical utility—a tool that makes the web more accessible and information more digestible.

Word Count: ~5,000 words

This blog post documents the development journey of the TL;DR Content Summarizer WordPress plugin and Chrome extension, from initial concept to sophisticated implementation with site-specific extractors.

Privacy Policy for TL;DR Content Summarizer Chrome Extension

Introduction

TL;DR Content Summarizer (“we,” “our,” or “the extension”) is committed to protecting your privacy. This Privacy Policy explains how we handle information when you use our Chrome extension.

Important: This extension does NOT collect, store, or transmit any personal data to our servers. All processing happens locally in your browser or through OpenAI’s API using your own API key.

Data Collection and Storage

We Do NOT Collect:

❌ No personal information – We do not collect names, email addresses, or any identifying information
❌ No browsing history – We do not track which websites you visit
❌ No content data – We do not store or transmit the content you summarize
❌ No usage analytics – We do not track how you use the extension
❌ No server-side storage – We do not have any servers that store your data

What IS Stored (Locally in Your Browser):

✅ OpenAI API Key – Stored securely in your browser’s local storage using Chrome’s storage API. This key is:

Stored only on your device
Never transmitted to our servers
Only used to make API calls to OpenAI on your behalf
You can delete it at any time through the extension settings

✅ Cached Summaries – Summaries are cached locally in your browser to:

Reduce API costs by avoiding duplicate requests
Provide instant access to previously generated summaries
Improve performance and user experience

✅ Extension Settings – Your preferences (model selection, verboseness, style, etc.) are stored locally in your browser

All local storage is managed by Chrome and can be cleared at any time through Chrome’s settings or the extension’s “Reset Cache” feature.

How Your Data Is Used

OpenAI API Integration

When you request a summary:

Content Extraction – The extension extracts content from the webpage you’re viewing. This happens entirely in your browser.
API Request – The extracted content is sent directly from your browser to OpenAI’s API using your API key. We do not intercept, store, or see this data.
Response Processing – OpenAI’s response is processed in your browser and displayed to you.
Local Caching – The summary is cached locally in your browser for future reference.

We never see, store, or have access to:

The content you’re summarizing
The summaries generated
Your API key
Any data sent to or received from OpenAI

Third-Party Services

OpenAI API:

This extension uses OpenAI’s API to generate summaries
All API calls are made directly from your browser to OpenAI
Your use of OpenAI’s API is subject to OpenAI’s Privacy Policy and Terms of Service
You are responsible for your own OpenAI API usage and costs
We recommend reviewing OpenAI’s privacy practices

Data Security

Local Storage Security

All data stored locally uses Chrome’s secure storage APIs
Your API key is stored using Chrome’s chrome.storage.local API, which is encrypted by Chrome
Cached summaries are also stored locally and encrypted by Chrome
No data is transmitted to our servers (we don’t have any)

API Key Security

Your OpenAI API key is stored locally in your browser only
The key is never transmitted to our servers
The key is only used to make direct API calls from your browser to OpenAI
You can view, change, or delete your API key at any time in the extension settings

Important Security Note:

Keep your OpenAI API key secure and private
Do not share your API key with others
If you suspect your API key has been compromised, regenerate it in your OpenAI account
You are responsible for any charges incurred through your OpenAI API key

Your Rights and Control

You Have Full Control:

✅ Delete Your Data – Clear all cached summaries using the “Reset Cache” button in the extension

✅ Remove Your API Key – Delete your API key from the extension settings at any time

✅ Uninstall – Uninstalling the extension removes all locally stored data

✅ Chrome Settings – You can clear extension data through Chrome’s settings:

Go to chrome://extensions/
Find “TL;DR Content Summarizer”
Click “Remove” to delete all data

No Account Required

This extension does not require you to create an account
There is no registration or login process
We cannot access your data because we don’t have accounts or servers

Children’s Privacy

This extension is not intended for children under 13 years of age. We do not knowingly collect personal information from children. If you are a parent or guardian and believe your child has provided us with personal information, please contact us.

Changes to This Privacy Policy

We may update this Privacy Policy from time to time. We will notify you of any changes by:

Updating the “Last Updated” date at the top of this policy
Posting the new Privacy Policy in the extension’s repository or website

You are advised to review this Privacy Policy periodically for any changes.

Contact Information

If you have any questions about this Privacy Policy, please contact us:

C2IT Labs

Website: https://c2itconsulting.net
Email: [Your contact email if available]

Summary

In simple terms:

✅ We don’t collect your data
✅ We don’t store your data on our servers (we don’t have servers)
✅ We don’t track your activity
✅ Everything is stored locally in your browser
✅ You use your own OpenAI API key
✅ You control all your data
✅ You can delete everything at any time

Your privacy is our priority. This extension is designed to work entirely client-side, with no data collection or server-side processing.

Compliance

This extension is designed to comply with:

General Data Protection Regulation (GDPR)
California Consumer Privacy Act (CCPA)
Other applicable privacy laws

Since we do not collect, store, or process any personal data, the extension inherently respects your privacy rights.

This Privacy Policy is effective as of the date listed above and applies to all users of the TL;DR Content Summarizer Chrome extension.

Building TL;DR Content Summarizer – A Journey in AI-Powered Content Curation

From WordPress Plugin to Chrome Extension: Building TL;DR Content Summarizer – A Journey in AI-Powered Content Curation

Introduction: The Problem That Started It All

Part 1: The WordPress Plugin – A Foundation for AI-Powered Summarization

The Initial Vision

Architecture and Design Philosophy

The Content Extraction Challenge

Caching Strategy

User Experience Features

The OpenAI Integration

Part 2: The Evolution to Chrome Extension – Taking It Beyond WordPress

The “What If” Moment

Technical Challenges of Browser Extension Development

The Side Panel Revolution

Content Extraction in the Wild

Part 3: The Site-Specific Extractor System – Extensibility by Design

The Problem with Generic Extraction

The Extractor Architecture

LinkedIn Extractor – A Case Study

Facebook Extractor – Another Example

Style Override System

The Extensibility Promise

Part 4: Advanced Features and Polish

Caching and Performance

Summary Styles – Making AI Responses More Useful

Verboseness Levels

Social Sharing Integration

Visual Design and UX

Part 5: The Development Journey – Git History Tells the Story

Initial Commit (5b017ac - TLDR plugin Initial commit)

Chrome Extension Initial Commit (381cea9 - chrome-extension-initial-commit)

Icon Development (cd47569 - icons)

Iterative Improvements

The Fun of Building

Part 6: Technical Deep Dive – How It All Works

WordPress Plugin Flow

Chrome Extension Flow

Content Extraction Algorithm

OpenAI Integration

Caching Implementation

Part 7: Challenges and Solutions

Challenge 1: Content Script Injection Failures

Challenge 2: Markdown Not Rendering

Challenge 3: TOC Links Not Navigating

Challenge 4: Summary Generation Hanging

Challenge 5: Facebook/LinkedIn Only Passing URL

Challenge 6: Site-Specific Extractors Not Applying Style

Part 8: Future Possibilities and Extensions

Potential Enhancements

Technical Improvements

Part 9: Lessons Learned

What Went Well

What Could Be Improved

Key Takeaways

Part 10: Conclusion – The Joy of Building Useful Tools

Technical Specifications

WordPress Plugin

Chrome Extension

OpenAI Integration

Acknowledgments

Privacy Policy for TL;DR Content Summarizer Chrome Extension

Introduction

Data Collection and Storage

We Do NOT Collect:

What IS Stored (Locally in Your Browser):

How Your Data Is Used

OpenAI API Integration

Third-Party Services

Data Security

Local Storage Security

API Key Security

Your Rights and Control

You Have Full Control:

No Account Required

Children’s Privacy

Changes to This Privacy Policy

Contact Information

Summary

Compliance

Recent Posts

Initial Commit (`5b017ac - TLDR plugin Initial commit`)

Chrome Extension Initial Commit (`381cea9 - chrome-extension-initial-commit`)

Icon Development (`cd47569 - icons`)