Thought leadership and technical depth
Keeping credentials out of your code
A password committed to a repository is a password you have to assume is public. How to manage secrets so a leaked laptop or a public repo is an inconvenience, not a breach.
Do you actually need a vector database?
Vector databases are the default answer for AI search — and often more than a project needs. When a dedicated vector store earns its place, and when your existing database will do.
Feature flags: shipping unfinished code safely
Feature flags let you merge and deploy code before it is ready to be seen, turn features on for a few users first, and switch off a bad release without a redeploy. Used carelessly, they become their own mess.
When to say no to a feature
Every feature you add is one you maintain forever. The health of a product depends as much on what you decline to build as on what you ship.
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.
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