Thought leadership and technical depth
Safe database migrations in production
A schema change that works on your laptop can lock a table and take the product down under real traffic. How to change a live database without a maintenance window.
What an AI feature really costs to run
A model call costs fractions of a cent — until you multiply it by real traffic. The costs that surprise teams are predictable if you look for them before launch, not after the first invoice.
Observability without drowning in dashboards
More dashboards do not make a system easier to run — they make it easier to miss the one thing that matters. What to actually monitor, and what to ignore.
Designing an API your customers will not hate
An API is a product with developers as its users. The ones people enjoy are predictable, honest about errors, and slow to break. A practical guide to the decisions that matter.
Prompt engineering is not the hard part
Clever prompts get the attention. The things that actually decide whether an AI feature works in production are less glamorous — context, evaluation, guardrails, and cost.
Choosing a database for a new product
The database is one of the hardest decisions to reverse later, and one of the easiest to overthink at the start. A practical way to choose without painting yourself into a corner.
How we estimate work we have never built before
A precise-looking estimate for novel software is usually a guess in a suit. Here is how we scope work honestly — ranges over false precision, and the discovery that makes the range narrow.
RAG in production: retrieval is the hard part
Retrieval-augmented generation demos in an afternoon and disappoints in production. The model is rarely the problem — the retrieval is. What separates a demo from something people trust.
The security questions enterprise buyers actually ask
Enterprise procurement runs on a short list of security questions. Knowing them ahead of time — and answering them honestly — is the difference between a deal that stalls and one that moves.
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