[et_pb_section fb_built=”1″ _builder_version=”3.0.47″ custom_padding=”0|0px|0|0px|false|false”][et_pb_row custom_padding=”0|0px|27px|0px|false|false” _builder_version=”3.9″][et_pb_column type=”4_4″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on”][et_pb_post_title author=”off” comments=”off” _builder_version=”3.9″][/et_pb_post_title][/et_pb_column][/et_pb_row][et_pb_row make_equal=”on” module_class_1=”vertical-center” module_class_2=”vertical-center” _builder_version=”3.11.1″][et_pb_column type=”2_3″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on” module_class=”vertical-center”][et_pb_text _builder_version=”3.11.1″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_padding=”|||”]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.
[/et_pb_text][/et_pb_column][et_pb_column type=”1_3″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on” module_class=”vertical-center”][et_pb_image src=”https://lh3.googleusercontent.com/cyCmbHn3n0S6fmwBmXC_95hQ1lEkx31_kYFptwLLOy0__2BCk_iL3WrnuBwTg7F_eEb-R9pJQf2XiLys-I6f5F4dSxmHuugMKXpVGIbRcQZi-4dy8QLWZMfIXNDtkm97Cwd8tKUq_IdQE5nPAnujwsagqRtxPju9MZms540SE8e8Zed0JLrUkwZM0s_IZWFIuobErWKHp04DLWQ1J4B1POPTvs9vnG-11uK1jYewxer2UpjD6ZCCYlhRgjfCpc8QZ1BqE6RpGIbB43hQVqgAGvMf-yzqDxPfY8G19n0aJp_5Dis00oManXxwFKjIyybfjAKrjVY2X-CUftoLFV643g68C37taec41okhfg271V3SaHD0GmOSizgZcHc7kF0IAA3tJMrGrE_zub7SOarGZ1beN2gsgXD7kEHEjCzzz5TPyB4qPn2GkbJ6FN9fccBx5AZNFyvpERw_Rct7JKKT5n5oha2dD8I5PaqIsmRwfYUO-yW0ffb3Qme4cKBKv4DnD8HQ3ZZtX6pBx41WEoUTnr_wngY2UqbR_hbZfn5S–OR05X5pJSBby67ufRLAPKCx5JWsN6JE4Wt6bfYo4WnqKeY7TVJ0B67OMFO6nhPZ7c4HRqTnUhsqidlc2urdgQ=w1000-no-tmp.jpg” url_new_window=”on” align=”right” _builder_version=”3.11.1″ border_radii=”on|100%|100%|100%|100%” custom_margin=”0px|||”][/et_pb_image][/et_pb_column][/et_pb_row][et_pb_row make_equal=”on” module_class_1=”vertical-center” module_class_2=”vertical-center” _builder_version=”3.11.1″][et_pb_column type=”2_3″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on” module_class=”vertical-center”][et_pb_text _builder_version=”3.11.1″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_padding=”|||”]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
[/et_pb_text][/et_pb_column][et_pb_column type=”1_3″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on” module_class=”vertical-center”][et_pb_image src=”https://lh3.googleusercontent.com/bv2q_SDmiPe1oxZYITQfsjoZ8s3IoLs5A0F3z1-JwpIzg8M8iIjcr6JdJQ_WS4SmXsqcVFRHXVSx8F4qGoLN5FgfFw1h6eNDx2kR3zkzek4hl-ho8Yg2dOsaJo58PyY02zXwe2W08sRZSJNIVLShPzW5VCWvffpGuB2dFXfr9AlB6uRJ2O1sSlLo3fhSXgUNZaPF87b1fJ1v5_JcuxuQAgY18eCJoi1ijc6z0Cy1NQ0rGd38A6Yi4SPWsqWfjg30oma627Kh6I5XL2gyL9bGPyxvtWGZI7tqxxGbL-iS_s218TbmlBzJU7i1xZSoFrWp1MVgqObxyCKUAcSzYZiEzJ-JL2TFzxE_xn2Q3vmOg6jIKqKhZBMOTFk5InAZcn-LwTD-DQS-tDXQeqNC8jqYB8e3rTkEdOrzP2BdVGwXq1BNyd1LeRyvKeid-VEFHAl9o3WqnESHlOrqOBm_5grHHmN1MmfsJx61ifwod4hZUCu_FFDRCMz6F1YhUetd82DVTV5czhnJeVFIAE8d_uSJ8yBIAtqo9tQj4lIk8L6aX9dONWNdVpObLWyIynJS5s9mmkGdGpJNcclZjHX2b9S8lV3puksm5WlMdhhsV8oCo0Gq4C3yBE43fJ6ZBPc7IT0=w1000-no-tmp.jpg” url_new_window=”on” align=”right” _builder_version=”3.11.1″ border_radii=”on|100%|100%|100%|100%” custom_margin=”0px|||”][/et_pb_image][/et_pb_column][/et_pb_row][et_pb_row _builder_version=”3.17.6″][et_pb_column type=”4_4″ _builder_version=”3.17.6″ parallax=”off” parallax_method=”on”][et_pb_code text_orientation=”center” _builder_version=”3.17.6″][/et_pb_code][/et_pb_column][/et_pb_row][et_pb_row custom_padding=”0|0px|27px|0px|false|false” _builder_version=”3.9″][et_pb_column type=”4_4″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.11.1″]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.

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