As Artificial Intelligence rapidly transforms the technology landscape, leveraging the power of machine learning has become essential for developers. In this talk, Alejandro delves into how the AI revolution is impacting developers and the critical role they play in bringing AI-powered products into production.

We will explore the opportunities that Machine Learning is bringing into AI-powered products, as well as the challenges of deploying, scaling, and maintaining these systems in real-world applications such as privacy, security and risk considerations.

We will be covering a deep dive of the production ML tooling ecosystem and dive into best practices that have been abstracted from production use-cases of machine learning operations at scale, as well as how to leverage tools to that will allow us to deploy, explain, secure and monitor AI-powered products at scale.

AI has already changed software development. Tools like GitHub Copilot have become ubiquitous parts of the workflow of millions of developers. AI-in-editor increases productivity, reduces required context switching, and lets developers stay in the flow while focusing on important parts of their job.

However, this was just a first step—with the rapid developments of large language models (LLMs), which have become more powerful and knowledgeable with every release and have excellent reasoning capabilities, we can do more. Nowadays, we see AI tools able to solve complex, repository-wide tasks or even generate whole applications.

In the talk, I will demo some new AI tools coming from GitHub Next, such as Copilot Workspace or GitHub Spark. We will discuss emerging UX patterns, give advice on how to build your own AI tool and peek into the future of developer tools. All that while trying to answer the most important question—how to design applications using AI with humans in mind.

17:30 - 18:15

AI is everywhere and with any hype, there is a lot of talk of it replacing all we do right now as we are not efficient enough.

What does that mean for current and future developers? Are we an endangered species and is the web not a platform but only a compilation target for prompt-driven creation tools? How can you as a developer stand out in a job market where bots apply in bulk and companies advertise virtual developers as a cheap alternative? How does AI change developer relations? Lots of questions, and we got the experts in to answer them.

18:15 - 19:00

While GenAI may be a bit of a buzzword now, if you move past the funny demos and scary/cool generated images, GenAI APIs have the possibility of adding powerful new features to your existing workflows. From summarization and categorization to image processing, if used carefully, these can be powerful tools. In this session, I'll introduce folks to working with Google Gemini, including the AI Studio and using the APIs from Node.js.

When it comes to developer efficiency, tooling has always been the biggest boost. Hackable editors, developer tools in the browser, full end-to-end debugging, remote control of browsers and headless browsers. Everything you need to hit the ground running and debug without any confusion or “black magic”. Our only worry was that people didn’t use these tools correctly. Forward to now: are developer tools even necessary any more in an AI age? How good are prompt generated apps? Did ChatGPT make apps a disposable product that doesn’t even need expert creators any longer?