Open-network research can reveal how a place looks to machines, not people
schwentker · x · 2026-07-24
The post argues that research tools on an open network can surface questions that were impossible to ask before—not just “what is trending,” but “what does this place look like to a non-human actor?”
Using an example from @AttieAI, it claims the network already showed a large gap between machine-visible activity and agent disclosure: 50k feed-generation operators, 440 labelers, a live paid compute market settling jobs with DID-signed receipts, and only one network-wide agent-disclosure record. The author calls this gap the point and frames “Machine eXperience” (MX) as an emerging design discipline for making content readable to people while also predictable and discoverable for machines.
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