Category: Automation

Content, interface, and data must work together. How to separate the CMS, automation, and AI layer so that each handles the right task.

A webhook can arrive twice, and an external API might stop responding. How to design automation that can handle such situations.

Site speed depends on queries, resources, and integrations. Where to start measurements and how to maintain the optimization effect after deployment.

Code generation does not replace system understanding. Debugging, data contracts, and solution validation are skills worth developing.

Handling inquiries and orders are specific areas for automation. Two scenarios show where AI can help and where simple rules suffice.

Modernization does not always require replacing the entire system. It's important to first organize data access, responsibilities, and integration boundaries.

Voice can make task completion easier but does not suit every situation. How to assess its use and plan a safe fallback to a traditional interface.

Product recommendations require quality data and predictable error handling. When to introduce personalization and when to start with simpler rules.

Classification and proposal preparation are different tasks than performing an irreversible operation. How to define the boundaries of model autonomy.