Google's just begun opening up access to an AI model I can actually get behind. This one lets you generate a virtual world of any kind and travel through it with a vehicle or character like in a video game – all with text prompts or images you upload.

The answer is simple. Start with boring. Boring projects make money. Clients who address these three elements are more likely to be among the 5% who succeed in their first AI project. Every business has tasks that are repetitive and error-prone. The first place to look for opportunity is where you have high employee attrition.

From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with projects that support AI development.

We are living through one of those rare moments when an entire industry cycle is being reimagined. Like the internet revolution of the 1990s, artificial intelligence is fundamentally reshaping how value is created and how competitive advantage is built.

Companies hate to admit it, but the road to production-level AI deployment is littered with proof of concepts (PoCs) that go nowhere, or failed projects that never deliver on their goals. In certain domains, there’s little tolerance for iteration ...

As part of the project, SynaXG used its complete AI-RAN software stack, including Layer 1, Layer 2, and Layer 3 microservices on Red Hat’s single cloud-native platform for both network and IT workloads – OpenShift.