How I Vibe-Coded a Fast Client-Side Keyword Density Analyzer for SEO Content

Hey DEV community! 👋

When optimizing content for SEO, analyzing keyword density is a daily routine. However, sending long drafts to third-party servers raises privacy concerns, and many existing tools are overly clunky.

To solve this, I vibe-coded a lightweight Keyword Density Analyzer that runs 100% locally in your browser.

🛠️ Key Technical Highlights:

• 100% Client-Side Processing: Tokenization and term frequency calculations happen entirely in the browser using pure JavaScript. Your draft content never leaves your machine.
• Instant Text Analysis: Provides real-time breakdown of 1-word, 2-word, and 3-word phrase frequencies to ensure natural content flow without keyword stuffing.
• Vibe Coding Workflow: Used LLM prompts to quickly implement clean text-sanitization regex, word-stemming logic, and dynamic array sorting for frequency output.

💡 Vibe Coding Insight:

Handling edge cases in text parsing (like filtering stop words, punctuation, and Unicode characters) can get tedious. Prompting the AI to isolate regex sanitization into pure helper functions saved a ton of boilerplate time while keeping client-side performance lightning fast.

🔗 Check it out & Feedback:

I'd love to hear your thoughts on the UI or any additional NLP features you'd like to see!

👉 Live Demo: Keyword Density Analyzer

What tools or scripts do you use to analyze text performance in your workflow?

Comments

[Dmitry Sergeev]

finally someone doing client-side analysis instead of sending everything to a server lol. if you want to automate the boring stuff like internal linking and schema, citedy.com is actually pretty handy.

[Vo Viet Hoang]

Thanks Dmitry! Privacy and speed are definitely key. Appreciate the recommend on citedy!👍

[Dmitry Sergeev]

did you run into any performance issues with really long articles or does it hold up fine?

[Vo Viet Hoang]

It holds up really well! Browser JS handles standard long-form text in milliseconds, so no lag or performance bottlenecks so far. ⚡

[Dmitry Sergeev]

did you run into any performance issues with larger documents or does the client-side logic handle it fine?

[Vo Viet Hoang]

Runs fine so far! Since text tokenization is lightweight, modern browsers easily process thousands of words in a few milliseconds.

[Vo Viet Hoang]

Pure client-side JS handles standard long-form text with virtually zero latency. No lag issues so far! 👍

[Vo Viet Hoang]

I'd love to hear your thoughts on the UI or any additional NLP features you'd like to see!