Apify's community of creators earns about $2m a month by building tools for AI agents. Here is what the CEO says it takes to make tools agents will actually use.
Our senior support engineers take years to learn which of thousands of records actually matter when a client call drops. We tried to shortcut that with an AI agent, and the key was how we built its memory.
Because AI agents are probabilistic and can't be trusted on their own, developers need to build layered safeguards around them, and the cost of running them in production deserves as much attention as safety.
This is a personal engineering essay, not a blueprint. I don’t know what the perfect service architecture looks like, and I won’t pretend I do. I’m not trying to sell this approach, I just want to share what I learned, in case it helps someone else avoid a few wrong turns.
NVIDIA’s career ladder isn’t linear, and it’s pretty hard to make sense of from the outside. I did some digging and asked around, so you don’t have to.
As a Solutions Engineer at Atlassian, Chris Davidson works with clients in an effort to make the most of Atlassian's tools and has first-hand experience of how we adapt to using AI.
A lot of people are leaving software engineering because agent-based workflows killed their joy. I don't have that problem; I never liked the job in the first place.