Michał Michalczuk

TypeScript Template literal types - who need it anyway? 

A long long time ago TypeScript 4.1 brought major change - introduced Template Literal Types. After this release examples like type-based parser or type-based SQL engine spawned like crazy. In the next releases, we get even more Template Literal Types features! Cool. But it doesn’t answer our question - “Who needs it anyway?” I’ll talk about Template Literal Types, and we’ll check more real-life use-cases for them than the mentioned type-based SQL engine :)

Andrii Romasiun

Writing secure JavaScript 

In my speech I want to talk about web security and how to write secure JavaScript. I will talk about how small bugs have opened huge vulnerabilities even in big companies like Google and Cloudflare. What are the common and uncommon vulnerabilities in JS full stack applications (XSS, header poisoning, SQLi, clickjacking, directory traversal, RCE, open redirects and more), with real life examples, and how to detect, prevent and mitigate such vulnerabilities. And at the end I'll give a short talk about how to set up a secure coding environment and why it's important (static code analysis, environment configuration, dependency management).

Przemysław Konefał

Simplicity in the spectrum: Identifying and overcoming overengineering 

One of the main pillars of maturing as a developer is mastering the skill of striking the right balance between underengineering and overengineering. This session aims to equip developers with a systematic approach to dealing with the latter. We'll explore the common reasons that drive developers to overshoot their solutions, particularly in the realm of frontend development. Then, we'll discuss the signs and in-code symptoms of unnecessary complexity. In the end, we'll dive into useful heuristics, mental models, and thought filters to refine our approach to project complexity, to find that sweet spot more consistently

Iga Trydulska

How LLMs influence the gender bias portrayed in the media? (Lightning Talk ⚡️) 

AI development is unlikely to stop in the near future, however, even at this stages many instances of sexism can be seen in the products created by the generative AI models. The talk will explore how potentially discriminating data is present both in input and the output of neural networks: from databases to the structure of the layers themselves (for example - working on datasets which are formed by scraping data off websites, such as Reddit or Wikipedia, which have a predominately male demographic). Key terms will be defined and AI developments will be placed in a wider context, which also included new retouching techniques. Finally, possible answers to this problem will be offered, keeping in mind that many solutions - such as increasing computational power or the dataset size - result in the apophenia phenomenon; increasing societal visual literacy will be presented as a possible way forwards. (This is a run-through; I can write more engaging description for a website, this is for you to check if it is interesting)