AI & Software

A 100,000-token context window sounds like perfect memory. It isn't. Here's what's actually happening inside that attention mechanism.

Adding features is celebrated. Removing them is where real engineering judgment lives, and most teams are terrible at it.

Git and its cousins do store your work, but that's the least interesting thing they do. Here's what version control is actually for.

A software team spent weeks feeding their model richer data and watched accuracy drop. Here's what went wrong and what it reveals about how LLMs actually process information.

Training your model is the starting line, not the finish. Here's what actually happens to model performance over time, and how to manage it.

Heisenbugs don't just frustrate developers. They expose a fundamental flaw in how we think about software correctness.

LLMs don't know when they're wrong. Understanding the three ways they fail silently is the skill that separates confident AI users from the ones who get burned.

Before transformers, before LLMs, before vector databases, there was one foundational move: turning meaning into math. Here's how it actually works.

A suppressed warning in Toyota's embedded software contributed to one of the costliest automotive recalls in history. The warning was real. The team just stopped looking.

AI model disagreement isn't a signal of healthy debate. It's a reliability crisis we've decided to call a feature.

A memory corruption bug that only appeared in production taught one team that non-reproducible failures aren't flukes. They're signals you haven't learned to read yet.

Spotify's Discover Weekly isn't magic. It's geometry. Understanding embeddings through the product that made them matter.

More code means more surface area for bugs, more cognitive load for engineers, and more ways for systems to fail. Deletion is a technical discipline, not a luxury.

Vector databases are genuinely useful tools. They're also being adopted by teams who can't yet articulate what problem they're solving.

A green deployment pipeline is a starting condition, not an ending one. The most expensive bugs in software don't appear until users arrive.

A clinical decision support project at a major health system collapsed not because the model was bad, but because nobody had thought clearly about the problem first.

Vendor lock-in has always been a risk. AI dependencies make it existential. The story of what happens when the model underneath your product simply disappears.

More instructions feel like more control. They aren't. Here's what actually happens when you pile rules into a system prompt.