Maya Chen

Adding features is celebrated. Removing them is agonizing. Here's why deletion is the most underrated skill in software.

Adversarial inputs look dangerous. The prompts that actually break LLM applications look completely ordinary — and that's the whole problem.

Engineers obsess over load balancers while their actual bottlenecks hide in databases, connection pools, and application code. Here's where to actually look.

A legal tech company spent months optimizing prompts that the model never fully received. What they discovered changed how their entire engineering team thinks about context.

A major streaming platform's 2021 outage traced back to a single bastion host. The lesson isn't about redundancy. It's about what we decide doesn't count as infrastructure.

The model hasn't changed. Your results have. Here's what's actually happening when your prompts start working.

Developers routinely conflate two distinct performance metrics, then wonder why their optimizations make things worse. The confusion is fundamental, not cosmetic.

A nanosecond is meaningless until you understand what your code does a million times per second. Here's the mental model that changes how you build systems.

Between your text and the model's attention, a lot happens. Understanding that gap changes how you think about AI behavior entirely.

Some bugs vanish the moment you try to find them. That's not bad luck — it's a structural property of complex systems. Here's what heisenbugs actually teach us.

Prompt engineering gets dressed up as a new discipline, but it's really just debugging a system whose source code you can't read.

Router contention isn't a failure case. It's the normal operating condition of the internet, and how it's handled explains a lot about why networks behave the way they do.

LLMs don't flag uncertainty, they just answer. That gap between confidence and accuracy is where real damage happens.

A stock trading firm lost millions on trades that happened in the wrong order. The culprit wasn't a bad algorithm. It was a clock.

Fine-tuning promises to make a general model fit your specific needs. It frequently does the opposite. Here's why, and what to do instead.

Spotify's recommendation problem reveals the core trick behind modern AI: words and concepts become vectors in geometric space, and similarity becomes distance.

Developers learn the textbook definition of race conditions early. Most never learn how many forms they take, or how quietly they can hide.

When you craft a prompt carefully, you're not writing instructions. You're adjusting a model's behavior through its input layer. That distinction matters.