Priya Sharma

You're probably doing your hardest thinking during email triage. That's not a time management problem — it's a task allocation problem.

The creator of Inbox Zero spent years building the perfect task system before realizing it was optimized for capturing work, not finishing it.

Meta's decision to release Llama models at multiple sizes taught the industry something counterintuitive: smaller, focused models frequently outperform their giant siblings in real deployments.

Over-engineered prompts often produce worse results than simple ones. Here's why, and what to do instead.

The skills behind good prompting aren't new. They're the same ones that make you a better writer, manager, and problem-solver.

The more capable the model, the more convincing its mistakes. This isn't a bug that will get patched. It's a structural feature of how these systems work.

The productivity system that promised to capture everything you know mostly captures everything you'll never revisit. Here's why, and what to do instead.

Most people treat AI models like colleagues who remember context. They don't. Understanding why changes how you work with them.

Retrieval-Augmented Generation is genuinely useful, but most teams deploy it expecting it to solve something it was never designed to fix.

Most prompt engineering advice focuses on getting better outputs. That's the wrong goal. Here's what to optimize for instead.

Chain-of-thought prompting genuinely improves LLM reasoning, but not for the reasons most people assume. Here's the real mechanism.

Your brain keeps processing an interruption long after you dismiss it. Here's what that costs you and how to design your day around it.

Time blocking feels productive. But for deep, creative work, it often destroys the conditions that make that work possible.

Vector databases don't understand language. They store coordinates. What happens between those two facts explains a lot about where AI search succeeds and fails.

Canceling a meeting isn't failure. Often it's the most productive decision in your calendar. Here's why, and how to make it a habit.

You write code in English-like syntax. The CPU speaks binary. Here's what happens in between, and why it matters more than you think.

When models train on AI-generated text, small errors compound into something worse than noise. This is a structural problem, not a data hygiene issue.

The friction in your productivity system is asymmetric. Adding takes one second. Finishing takes everything else.