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Multi-agent systems, where multiple agents (generative AI models + tools) collaborate, are emerging as an effective pattern for solving long-running, complex tasks in numerous domains. However, ...

According to Greg Brockman on Twitter, effective debugging in AI development sometimes requires developers to deeply analyze and understand the code, as traditional debugging tools may not always ...

Sometimes, reading Python code just isn’t enough to see what’s really going on. You can stare at lines for hours and still miss how variables change, or why a bug keeps popping up. That’s where a ...

Code.org CEO Hadi Partovi during an event in Seattle in July, announcing a new “Hour of AI” campaign to demystify AI in the spirit of the group’s past “Hour of Code” initiatives. (GeekWire Photo / ...

What if your code could think beyond syntax, anticipating bugs, predicting outcomes, and even reasoning through complex problems? Enter Meta’s Code World Model (CWM-32B), a innovative leap in ...

What if you could delegate the most tedious parts of your coding workflow to specialized assistants that never lose focus, make fewer mistakes, and adapt seamlessly to your needs? Enter Claude Code ...

AI Still Struggles to Debug Code, But for How Long? Many of the world's most popular AI tools, such as those from OpenAI and Anthropic, are not yet debugging pros, according to Microsoft Research, but ...

There are few areas where AI has seen more robust deployment than the field of software development. From “vibe” coding to GitHub Copilot to startups building quick-and-dirty applications with support ...

Ever feel like you’re spending more time squashing bugs than actually building something? You’re not alone—developers spend a whopping 35% of their time debugging and reviewing code instead of writing ...