AI-enhanced UX
Alexandra Anghel is co-founder and software engineer at MorphL – a platform that uses machine learning to predict users’ behavior in mobile & web applications. MorphL is my second startup, I’ve also co-founded Appticles, a platform for creating progressive web apps. Before starting Appticles, I owned an outsourcing company. I’m co-founder of Codette, a community for women interested in IT&C. Codette promotes education at all levels and create opportunities for women to fulfill their potential through workshops, meetups, conferences, hackatons and grants.
Ciprian Borodescu is a tech-business in-between guy, passionate about entrepreneurship and building product teams. In 2005 he started his first web agency, grew it to a decent size with customers all over the world. In 2010 went through Startupbootcamp business accelerator in Copenhagen/Denmark. In 2014 raised a seed round (up to 200,000 eur) for Appticles.com from LaunchHub (EU). In 2016 he raised a second seed round from Prosper Women Entrepreneurs in St. Louis, Missouri (US). Since 2015 he’s the organizer of BucharestJS – a JS developer group in Bucharest which grew to one of the biggest in the region with over 3,000 members; he founded and co-organized JSHacks – a series of JavaScript Hackathons happening at the same time in different cities across EU. In 2018 – Proud recipients of Google Digital News Innovation Grant (50,000 eur) to develop MorphL as an open source project that uses machine learning to predict user behaviors in mobile/web applications.
He’s passionate about #entrepreneurship, #web, #blockchain and #ai … not necessarily in this order. His personal blog is cborodescu.com
If we agree that building for the user is our main goal as developers, I think we can also acknowledge that this is a process that requires multiple iterations — a process that developers seldom navigate by looking at the data. Usually, there’s somebody else, be it a product owner, marketing or salesperson, analyzing it and feeding developers a feature list needed for the next product release. There lies the gap between developers and users which leads to lots of guess-work.
How can we remedy this and how can we accelerate this process? What if product micro-metrics could be directly integrated into the user-facing product components? And what if we could build these components to automatically adapt to users’ behaviors based on micro-metrics and provide a personalized user experience?
Today through the use of machine learning it is possible to optimize user interactions by measuring product micro-metrics and automatically adapting user-facing components to provide a personalized user experience.