As we continue to develop NLPCaptcha, we’ve encountered and overcome several technical challenges. Today, I want to share some insights into these challenges and how we’ve addressed them using Python and various NLP techniques.
In 2008-2009, as mobile technology was beginning to transform various industries, I had the opportunity to be part of a groundbreaking project at MPower Money in New Delhi, India. As a Software Engineer, I was instrumental in developing PHIRE, the world’s first mobile debit network that allowed users to perform banking transactions via SMS, leveraging the power of Python and advanced metaprogramming techniques.
As an early founding engineer I’m excited to share our journey in developing NLPCaptcha, a revolutionary technology that’s set to transform both web security and digital advertising.
Traditional CAPTCHAs, while effective in distinguishing humans from bots, have become a source of frustration for users. They often involve deciphering distorted text, which can be time-consuming and irritating. Moreover, they represent a missed opportunity for website owners and advertisers.