The benchmarks driving AI development have a fatal flaw: they reward performance over competence, eloquence over accuracy. Understanding this gap is the first step to building, and choosing, systems that actually work.
AI systems are suddenly capable of tasks they consistently failed at months ago. The secret isn't just about bigger models or more computing power. Behind the latest breakthroughs is a quiet revolution: companies have stopped training AI and started teaching it.
Post-training is all the rage. Are we going from training to teaching? What does a curriculum look like for LLMs? Let's find out.
MCP is fast becoming the "USB-C for AI," connecting models to the real world. This post breaks down what it is, why it matters, and where it's headed.
