Ideas, engineering, and lessons from the field.
Practical thinking on AI, software, infrastructure, and building technology that works in the real world.
Build vs Buy: How to Make Better Technology Decisions
The build-vs-buy question is usually framed as cost vs speed. The more useful question is what's actually core to your business and what isn't.
Connecting AI Systems to Your Existing Business Software
The hardest part of adding AI to your stack usually isn't the model. It's making it work reliably with the systems you already depend on.
How to Evaluate an AI Use Case Before Spending on It
Five questions that separate AI ideas worth funding from ones that will stall, asked before any budget is committed.
Why Automations Fail Silently and How to Catch It
The dangerous failure is the one nobody sees. Five causes of silent failure, and the monitoring that makes each of them visible.
Redis Caching Patterns for High-Performance Applications
Caching solves performance problems and creates consistency problems. Choosing the right pattern for each use case avoids the second half of that trade.
How to Choose the First Task for an AI Agent
The first agent project decides whether a company builds a second one. Four tests help pick a task that is useful, safe and measurable.
Which Business Processes Are Worth Automating First
A simple scoring method for choosing automation candidates: how often the work happens, how stable it is, and what a mistake costs.
Docker and CI/CD: Building a Reliable Deployment Pipeline
A deployment pipeline you can trust doesn't happen by accident. It's a specific set of practices, most of which are cheap to build in from the start.
Cloud Architecture for Growing Applications: Scaling Without Overspending
Cloud costs rarely grow because of bad architecture. They grow because nobody revisits sizing decisions after the initial launch.
Have a problem you're trying to solve?
We're always happy to talk it through, no pitch required.