Tech & Software

Vision-First AI: From Datasets to Deployments Vision-first AI puts the end goal first. It connects the user need, the data that can satisfy it, and the deployment that makes the result useful. By planning for deployment early, teams reduce the risk of building a powerful model that never reaches users. This approach keeps product value in focus and makes the work communicable to stakeholders. Start with a clear vision. Define the problem, the target metric, and the constraints. Is accuracy the only goal, or do we also care about cost, latency, and fairness? Write a simple success story that describes how a real user will benefit. This shared view guides both data collection and model design. ...

SIEM, Logging, and Observability in Modern Apps Modern apps rely on data to stay secure and reliable. Logs, metrics, and traces help teams understand what happened, when it happened, and why. SIEM focuses on security events and threat detection, but it works best when it sits alongside good logging and strong observability. Observability means you can explain system behavior from the data you collect, not just react to alerts. Together, these practices form a strong foundation for safer, faster software. ...

Pragmatic API Design for Interoperability Interoperability means that services from different teams or systems can talk to each other with minimal extra work. A pragmatic API design keeps the surface small, predictable, and well described. It helps developers build reliable integrations and reduces surprises during production. Start with a clear contract. The contract defines the surface you expose, the data you expect, and what you return in each case. Keep resources stable, avoid sudden breaking changes, and document the intent behind fields and endpoints. A simple, honest contract makes it easier for downstream teams to adapt. ...

Building APIs: Design, Documentation and Security APIs are the roads that connect apps, services, and people. A good API design, clear documentation, and solid security work together to reduce friction and risk. This guide gives practical ideas you can apply today. Design Good design starts with clear resources and predictable behavior. Practical principles: Use nouns for resources (tasks, users) and pluralize lists. Be consistent: the same verbs, status codes, and error formats across endpoints. Plan versioning early and document it in the spec. Keep endpoints small and focused; avoid overfetching data. Define pagination, filtering, and sorting in a uniform way. Example endpoints for a simple task API: ...