
Refined GitHub
GitHub for Mobile
GitHub for Atom
Board for Github
GitStreak
CommitTasks
Co-commit
Now add co-authors to your commits

Riskified
Kount
Sift
ClearSale
iovation
AppsFlyer
Radial
Signifyd is a SaaS-based, enterprise-grade fraud technology solution for e-commerce stores.

Which is more popular?
Signifyd might be a bit more popular than Commit Together by Github. We know about 1 link to it since March 2021 and only 1 link to Commit Together by Github.
Website, pricing, platforms and company facts side by side.
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| Website | github.blog | signifyd.com |
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What each product offers, as listed by its team.


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Signifyd Review: Top Cybersecurity Review Companies - AngelKings.com
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Share your experience with using Commit Together by Github and Signifyd. For example, how are they different and which one is better?
Recommendations tracked on public social media and blogs since March 2021.


There is "Co-authored-by" which is supported on GitHub [1] and seems appropriate if the maintainer is basing the solution on someone's code. [1] https://github.blog/2018-01-29-commit-together-with-co-authors/. - Source: Hacker News / over 4 years ago
There are third party solutions to fraud that actually work, providing chargeback insurance. Essentially, they screen transactions; if any approved transactions are chargebacked, they refund you. A good start point is... - Source: Hacker News / about 4 years ago
When comparing Commit Together by Github and Signifyd, you can also consider the following products.

Browser extension that makes GitHub cleaner & more powerful
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eCommerce fraud prevention solution and chargeback protection guarantee for online merchants. Find out how we can help your company boost revenue from online sales using our machine-learning powered eCommerce fraud protection software.
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The world’s development platform, in your pocket
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eCommerce fraud detection & prevention
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Git and GitHub integration right inside Atom
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Digital Trust & Safety enables your business to grow, innovate, introduce new products, features, and business models – without increased risk.
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