One of the most impactful applications of MCP is its ability to connect AI coding assistants directly to developer tools.

AI is brilliant at polishing and rephrasing. But like a child with glitter glue, you still need to supervise it.

How to close the loop between user behavior and LLM performance, and why human-in-the-loop systems are still essential in the age of gen AI.

We could soon find ourselves deferring to AI assistants that botsplain our every experience in real time. Is this empowerment or deferral?

The challenge is not just to build better AI tools, but to ask harder questions about where they are taking us.

GUEST: Consider maintaining and developing an e-commerce platform that processes millions of transactions every minute, generating large amounts of telemetry data, including metrics, logs and traces across multiple microservices. When critical incidents occur, on-call engineers face the daunting task of sifting through an ocean of data to unravel relevant signals and insights. This is equivalent to […]

AI coding requires a serious structural change. Where does that leave entry-level developers and the software industry as a whole?

GUEST: The past few decades have seen almost unimaginable advances in compute performance and efficiency, enabled by Moore’s Law and underpinned by scale-out commodity hardware and loosely coupled software. This architecture has delivered online services to billions globally and put virtually all of human knowledge at our fingertips. But the next computing revolution will demand much […]

To reflect democratic principles, AI must be built in the open. If the U.S. wants to lead the AI race, it must lead the open-source AI race.