Websites Using Giscus

Giscus is a comments system powered by GitHub Discussions that allows website owners to embed discussion threads on their pages. It enables visitors to comment using their GitHub accounts while discussions are stored in a GitHub repository.

Categories: Comment system

Official website: https://giscus.app

16 websites using Giscus

• csdiy.wiki
• boathousestores.com
• happycasastore.it
• bulksupplements.com
• productosdelujo.cl
• brookstone.com
• mirrorkomik.info
• quartzcomponents.com
• tekkietown.co.za
• venus.com
• tupperware.com
• startfitness.co.uk
• mix.tokyo
• supersports.co.th
• fast.ai
• presonus.com

Giscus is ranked #10 of 20 in Comment system by website adoption.

Comment system alternatives ranked by popularity

1. Facebook Comments — 8,373 websites
2. Disqus — 1,789 websites
3. Annoto — 0 websites
4. Cackle — 0 websites
5. Chirpy — 0 websites
6. Commento — 0 websites
7. Coral — 0 websites
8. Cove — 0 websites
9. Cusdis — 0 websites
10. GraphComment — 0 websites

Technologies commonly used with Giscus

• Open Graph — 94% co-occurrence
• HSTS — 94% co-occurrence
• Cloudflare Bot Management — 88% co-occurrence
• Cloudflare — 88% co-occurrence
• Cloudflare WAF — 88% co-occurrence
• cloudflare-argo — 88% co-occurrence
• reCAPTCHA Enterprise — 81% co-occurrence
• Dev.to — 81% co-occurrence

How WhatStack detects Giscus

WhatStack detects Giscus by analyzing HTTP headers, HTML source code, JavaScript globals, meta tags, cookies, DNS records, and ads.txt entries using signature-based pattern matching across 10 different analyzers.

Frequently Asked Questions

What is Giscus?

Giscus is a comments system powered by GitHub Discussions that allows website owners to embed discussion threads on their pages. It enables visitors to comment using their GitHub accounts while discussions are stored in a GitHub repository.

How many websites use Giscus?

WhatStack has detected Giscus on 16 websites in our database.

How do you detect Giscus?

We analyze HTTP headers, HTML source, JavaScript globals, meta tags, cookies, DNS records, and ads.txt entries to identify Giscus with high accuracy using signature-based pattern matching.