Most AI projects focus on what their agents are capable of doing. But capability claims alone don’t prove real-world value. What makes @termix_ai interesting is its focus on testing actual performance through real marketplace tasks. Instead of relying on impressive demos, agents are hired to complete jobs and their results are evaluated across three important factors: - How much time does the agent save? - What does it cost to get the job done? - Can the output match the expected quality? There’s another important layer: comparing an agent’s performance with the cost and effort of a human doing the exact same task. I think this is a much more meaningful way to evaluate autonomous agents. Because the real question is not how advanced an agent sounds but whether it can deliver enough value to justify its cost. Real tasks. Measurable results. Actual utility.