Ai

235 posts filed under Ai.

Before your AI feature ships to EU customers, try deleting one customer from everything it reads.

I've worked with data teams where someone spotted the broken number first and decided not to mention it.

A CTO wanted a full platform rewrite before the next raise. 8 weeks of tickets pointed at two components.

AI writes a working pipeline in 10 minutes. Nobody has asked it to be on call for one.

Companies are writing one job ad for three different jobs and calling it an AI data engineer.

A broken pipeline costs you an hour. A wrong number nobody catches costs you a quarter.

NIS2 turned one client's logging gap into a board-level problem inside a week.

A dataset with no owner and no cost ceiling will always be somebody else's problem.

Senior data engineers quit stacks that page them at 3am, and the exit interview says "growth".

A backend deploy broke the data pipeline for the third time this quarter. Both teams had done their jobs correctly.

Want expert eyes on your data architecture?