AI & Software

Vector databases don't store meaning. They store geometry. Understanding the difference changes how you build with them.

The concept of 'done' in software is a convenient fiction. Here's why that's not a problem to solve, but a reality to design around.

The skills behind effective prompt engineering aren't new. We've been doing this work for decades under different names.

Between your words and the model's attention lies a pipeline you didn't design and probably can't see. Here's what's actually happening to your prompt.

You probably think of embeddings as a search feature. They're actually closer to the connective tissue of modern AI-powered software.

Quantization and pruning shrink models efficiently, but they also change what the model is. The weirdness is worth understanding.

The skills that make you good at writing READMEs and API docs are the same ones that make you good at prompting LLMs. This is not a coincidence.

AI coding tools make you faster. They also quietly erode the understanding that makes you a good engineer. That tradeoff deserves more honesty.

Everyone explains embeddings as 'turning words into numbers.' That's not wrong, but it misses what makes the idea powerful and why it matters.

When you ask an LLM to 'think step by step,' something real happens. But it's not what reasoning looks like inside a human brain.

Vector databases don't store documents or images. They store numerical representations of meaning, and that distinction changes everything about how search actually works.

When their fraud detection pipeline silently failed for six days, the logs were full. They just didn't contain what anyone needed.

Transformers run the modern AI world, but 'attention' is one of the most misexplained ideas in the field. Here's what it actually does.

The uncomfortable truth behind noisy training data: it's not negligence. For many AI teams, dirty data is a deliberate engineering trade-off.

Developers obsess over complex algorithms while integer overflow quietly corrupts financial records in a date formatter written in 2009.

The ability to write a sonnet and the ability to count letters in a word are not on the same axis. AI training rewards one and ignores the other.

A green build means your tests passed, not that your software works. These are different things, and confusing them is expensive.

Vector databases power most modern AI search and retrieval. Here is what they actually contain, and why it matters for understanding how AI works.