AI Engineering focuses on building intelligent systems, while Data Science focuses on insights and predictionsBoth careers offer high salaries and ...

At random, I chose glm-4.7-flash, from the Chinese AI startup Z.ai. Weighing in at 30 billion "parameters," or neural weights, GLM-4.7-flash would be a "small" large language model by today's ...

AI-powered penetration testing is an advanced approach to security testing that uses artificial intelligence, machine learning, and autonomous agents to simulate real-world cyberattacks, identify ...

Python libraries handle real business tasks like APIs, data analysis, and machine learning at scaleUsing ready-made libraries ...

Something extraordinary has happened, even if we haven’t fully realized it yet: algorithms are now capable of solving ...

Software developers have spent the past two years watching AI coding tools evolve from advanced autocomplete into something ...

Last week, I developed the agentic AI brainstorming platform, an application that lets you watch two AI personalities (Synthia and Arul) have intelligent conversations about any marketing topic you ...

Apple is transforming Siri into an AI chatbot to rival ChatGPT. Discover its new features, challenges, and launch details here.

Anthropic has published new research on 29 January 2026 that asks a hard question of software teams adopting AI assistants: do quick productivity gains come at the cost of long‑term coding mastery?

The good news? This isn’t an AI limitation – it’s a design feature. AI’s flexibility to work across domains only works because it doesn’t come preloaded with assumptions about your specific situation.

AI-assisted programming is becoming increasingly prevalent. However, only experienced developers achieve productivity gains.