Legacy software creates a strange kind of tension.

AI projects rarely fail because the model was not powerful enough.

Legacy software is rarely “bad” software.

CEOs should evaluate AI investments by asking whether the investment improves a measurable business outcome, changes a real workflow, keeps humans appropriately involved, has a realistic path to production, and creates value that outweighs cost and risk.

Many organizations know they need to modernize their legacy applications. They also know why they haven't.

A widely used medical reference application built with Xamarin had become increasingly difficult to maintain as platform support deadlines approached. The client needed to modernize the application before operating system changes and framework deprecations created larger compatibility risks.

If you have ever spent twenty minutes debating an obscure AWS Lambda invocation pattern with an AI, you might question your life choices. But here I am: amused by the wasted time but ultimately happy with the outcome and understanding I gained.

If an app tracks user activity, Apple requires them to declare all information they collect as well as whether that data is linked or tracked. This includes collection by the app itself and any third parties the app uses. The app owner is responsible for knowing and correctly reporting privacy information for all components in the app.

Another Friday afternoon, another deployment fire. If this sounds familiar, you're not alone. On too many projects, the chasm between application code and infrastructure management breeds manual configuration nightmares, crippling complexity, and agonizingly slow development cycles. But what if your team could sidestep this chaos, focusing on building features instead of constantly battling deployment gremlins?