Priya Sharma

The meetings you didn't schedule are harder to fix than the ones you did. Here's how to find them and take your calendar back.

AI models display confidence scores as if they're meaningful quality signals. They're not. Here's what's actually happening under the hood.

AI coding tools produce fluent, well-structured code. They also produce bugs rooted in context they never had. Here's how to work with that limitation.

You probably already know when you do your best work. The problem is you've handed that time to other people.

Canceling a meeting isn't a failure of coordination. Sometimes it's the most productive decision you can make — and the work proves it.

Bigger AI models aren't always better. Smaller, focused models are often faster, cheaper, and more accurate for real tasks.

A product team built an elaborate knowledge management system and watched their collective memory get worse. Here's what went wrong and what it teaches us.

Chronic procrastination usually isn't a willpower problem. It's a signal that you're working on the wrong thing entirely.

You wrote one document. Your readers each reconstructed a different one. That gap is not a communication problem — it's a document design problem.

Most people know roughly when they do their best thinking. They schedule meetings there anyway. Here's how to stop.

A product team shipped features on time, but their velocity kept dropping. The problem wasn't what they hadn't finished. It was everything they almost finished.

Every to-do app is optimized for capture. None of them are optimized for completion. Here's why that matters and what to do about it.

Canceling a meeting feels productive. But the real skill is recognizing which ones shouldn't exist at all — and why they keep getting created anyway.

Bigger AI models get the headlines, but the real performance gains often come from making models smaller. Here's why constraints produce better reasoning.

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.