Artificial Intelligence

It’s no longer about fixing syntax errors - it’s about figuring out why a billion-parameter model did something totally unexpected.

After months of work, your AI agent can run tasks, create content, even make decisions. Exciting - but how do you use it safely and effectively in the real world?

AI moves faster than your last commit - and so do hackers. Security can’t be an afterthought; it has to run alongside your code, like invisible, always-on seatbelts keeping users safe.

What if your AI assistant could juggle flights, messages, and security checks all at once? Filip Srnec showed it’s possible.

Meet AgentKit, OpenAI’s drag-and-drop playground that handles all the boring LLM wiring for you.

Gone are the days of babysitting your AI. As Gift Egwuenu showed at Infobip Shift, agents now think and act for themselves - planning, booking, and getting things done.

You finally built that AI agent. It writes code, drafts emails, maybe even runs tasks on its own. It’s powerful, useful - and ready to ship. But then reality hits: how do you actually price something like this?

The logic behind a simple game of 'Guess Who?' is identical to how we code one of the most transparent AI algorithms. In Decision Trees, we don’t guess - we ask the question that gives the most information, and mastering that intuition teaches the core of predictive Machine Learning

What happens when two engineers turn Dungeons & Dragons into a testing ground for AI? They end up with a working AI-powered game engine that doubles as a blueprint for building more intelligent, reliable agentic systems.

Whether AI will replace human developers has become a typical headline. A recent talk at the Infobip Shift conference in Zadar took a more subtle approach: The future of software development isn’t a human-versus-machine battle but a new kind of collaboration.

What happens when AI agents stop just chatting and start acting, collaborating, and transforming business - powered by developers behind the scenes? Magic!

So, in 2025, which should you choose: PyTorch for fast-paced AI experimentation or TensorFlow for rock-solid production - and could the real answer be BOTH?

84% of developers now use AI daily - mostly LLMs. They’re great for cutting workloads, but risky in the wrong spots. Here are 5 times AI shines, and 5 times it can totally wreck your work!