Our Tech Stack

Uizard Technologies

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Posted on Apr 5, 2025

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Our Tech Stack

TL;DR: Tools, languages and libraries that power Uizard.

Team Operations

Technical Side

Research that Drives our Tech

Team Operations

Being remote-first, we need to make sure we have the tech to support operations, cross-team collaboration, and alignment. Across Uizard, here are some tools that we use:

Slack for internal comms

Notion for our project management and internal Wiki + Google Workspace

Google Meet for calls

Technical Side

When it comes to the technical side, we use a variety of tools, libraries, and languages. On the front end, we use Javascript/Typescript, Node.js, and React with tools like Cypress and Jenkins to support the team. On the back end (and insights side), we use AWS, python, dbt, Streamlit and Dash.

Research that Drives our Tech

Explore the research and technology that makes Uizard possible.

The scientific contributions listed on this page showcase some of the technology powering Uizard. The presented methods, experiments, and results are published for educational purposes only. We share the science, but we keep some secrets. 😉

How ‌deep‌ ‌learning‌ ‌is transforming design:‌ NLP and CV applications.

If you have ever tried creating a user interface, you probably quickly realized that design is hard. Choosing the right colors, using fonts that match, making your layout balanced... And all of that while keeping your users' needs in mind!

https://towardsdatascience.com/how-deep-learning-is-transforming-design-cv-and-nlp-applications-4518c50690e6

pix2code: Generating Code from a Graphical User Interface Screenshot

Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications. In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically generate code from a single input image with over 77% of accuracy for three different platforms (i.e.

https://arxiv.org/abs/1705.07962

Improving UI Layout Understanding with Hierarchical Positional Encodings

User interfaces often contain a rich hierarchy within the sets of elements that make it up. For example, a list may contain several list items, each list item may contain a card object, and this card object may contain an image, a paragraph, and an icon.

https://towardsdatascience.com/improving-ui-layout-understanding-with-hierarchical-positional-encodings-b19e1e9235e

Generating design systems using deep learning

How do you enable people to easily design beautiful mobile and web apps when they have no design background? "Easy: you just throw some neural networks at the problem!" "Well, kinda" You might have heard of Uizard already through our early machine learning research pix2code or our technology for turning hand-drawn sketches into interactive app prototypes.

https://tbeltramelli.medium.com/generating-design-systems-using-deep-learning-abe8d1195960