XNoBot’s: designing a reversible account filter without calling every small account a bot

AI assistance disclosure: this text was prepared with ChatGPT assistance for the project initiator.

I am exploring XNoBot’s, an independent early-stage project that would give people more control over which accounts appear in their experience on X. There is no public app or demonstrated bot-detection accuracy to announce.

The initial rule would hide content only when an account has fewer than 10 followers, fewer than 10 accounts followed and fewer than 10 posts. All three conditions would be required. The numbers are a starting hypothesis to evaluate, not evidence that an account is automated.

That limitation shapes the design: real newcomers and quiet users could be hidden, while established bots could pass the filter. This should be an explicit personal preference with reversible controls, not an accusation about other users.

Questions to resolve before promising a product

• Data access: Can the necessary counts be obtained reliably and affordably under the platform's current access conditions? Unknown or stale values should never silently become zero.
• User control: Thresholds, exceptions, a temporary disable control and a clear way to reveal hidden content need evaluation.
• Privacy: What can be processed locally, and what information would an integration require? Permissions and data collection should be minimized.
• Evaluation: We need to measure unwanted hiding of legitimate accounts and usefulness to volunteers, rather than claim an unmeasured bot-detection rate.
• Scope: An initial browser extension is an option to investigate; coverage across X surfaces and devices is not established.

For developers who have built personal content filters: which failure mode would you investigate first? Would an explanation such as “hidden by your account-metrics rule” and a one-click reveal make this useful, or would false positives outweigh the benefit?

I use the project alias as a privacy precaution because social-media manipulation is politically sensitive; this is not a claim of specific threats. The intended funding model is voluntary personal contributions of any amount, without institutional funding or donor control.

Project overview and optional contribution details

Collaboration contact: [e-mail]