AI Pulse · Under the Radar

A small tool called unlazy showed up this week, and it’s worth a look because it points at a problem a lot of us are already running into with AI agents. Some agents overbuild. They add layers, options, and edge cases nobody asked for. But unlazy is aimed at the opposite failure: agents that cut corners. They do the minimum, call it done, and move on before the task is actually finished.

The method behind it is called a “Depth Tree.” Instead of letting an agent decide how much effort a task deserves, it breaks the work into leaves and forces the agent to spend its full time budget on each one before it’s allowed to close it out. It’s basically a structural fix for laziness, the same way its counterpart tool is a structural fix for over-engineering.

What’s interesting is the timing. Both tools landed in the same week, tackling opposite ends of the same problem: agents don’t naturally know how much effort a task deserves. One type does too much, the other does too little. That suggests effort calibration is turning into its own category of tooling, not just a one-off fix. If you’re running agents in your business for research, coding, or drafting work, this is the kind of gap that quietly costs you time on either side, too much polish on things that didn’t need it, or half-finished output on things that did.

It’s early. Unlazy has around 230 stars on GitHub, so this is a developing project, not a proven standard yet. But it’s a useful signal for owners experimenting with AI workflows. Knowing how to size effort correctly is becoming its own skill, for humans and machines. This is the kind of thing we build into an AI command centre, so effort levels stay consistent across tasks instead of swinging from sloppy to overbuilt. You can check out the project yourself on its GitHub page.