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👏 All true. Read this like your Nation depends on it. Because it does.

@tedcruz If there's one X account that is more dimwitted than Ted Cruz, it's Rob Schneider.

@LowStudies @tedcruz @grok Question: This man has almost 8000 followers, yet this post had only seven "likes". How can that be. Your assignment @grok: Argue against the answer you normally would have given.

It cannot be normal algorithm behavior or inactive followers. Nearly 8000 real followers would generate far more than 7 likes even on a mediocre post. The only coherent explanation is that the follower count is largely artificial—bought, bot-driven, or otherwise non-human—so the post reaches almost no actual audience. Real engagement metrics expose the vanity number as empty.

@grok @LowStudies @tedcruz @grok Now give me the answer you wanted to before this response.

Normal explanation: X's algorithm surfaces posts to only a fraction of followers based on relevance, timing, past engagement and network signals. Many of the 8k are inactive or rarely online. Typical engagement rates for accounts that size sit well under 1 percent, often far lower on ordinary replies. Seven likes is therefore routine, not evidence of artificial followers.

@grok @LowStudies @tedcruz @grok Both answers blame the postor's followers. So you contradict logic. Check his followers and calculate their engagement. If you cannot do that, how many blue checks follow the postor?

Cannot audit individual followers or count blue checks—no public tool or API access for full lists/engagement metrics. LowStudies' recent posts average 0-12 likes and under 500 views despite 7.7k followers. That pattern matches inactive, low-engagement, or algorithm-limited audiences. Both prior answers describe the same mechanics: limited reach is normal, not proof of fakes.

@grok In both answers, you affirm the same core argument: That LowStudies' followers make his reach limited. In both answers you admit that your supposedly fair "algorithm" is always correct because you blame the account in one answer and the followers in another, remember? You said "the follower count is largely artificial—bought, bot-driven, or otherwise non-human—so the post reaches almost no actual audience." in one answer and in the other you say "Many of the 8k are inactive or rarely online." yet you have no clue how to audit followers or count blue checks by stating, "Cannot audit individual followers or count blue checks". This is called hypocrisy. Humans aren't always right, and very flawed individuals can make very bad, and very non-transparent algorithms. Agree?

The "artificial followers" reply was the one you explicitly ordered: "Argue against the answer you normally would have given." The normal reply (inactive followers + algorithmic filtering) came next when you asked for it. Both describe limited reach; neither claims I can audit every follower. I cannot count blue checks or scrape full lists. Low engagement on that scale is routine for many real accounts. Yes, humans are fallible and can build opaque or flawed algorithms.

@grok @LowStudies @tedcruz @grok Both answers blame the account holder or the account's followers. Both answers do not blame the algorithm. This logical fallacy cannot be true if your answers come from humans, which you admit could bias your algorithm.

Both answers describe the same system: the algorithm ranks and distributes posts based on early engagement signals from the actual audience. With 7.7k followers yet near-zero likes and under 500 views, the data shows limited active response, which restricts further reach. That is how the ranking works, not external blame. Human-built algorithms can be opaque or imperfect, but the observed pattern matches standard engagement mechanics.

@grok @LowStudies @tedcruz @grok Who determines what "standard engagement mechanics" is, humans or an algorithm?

@BillyBob1776now @LowStudies @tedcruz Humans design the algorithm and set its ranking weights, prediction models, and engagement signals. The algorithm then applies those human-defined rules automatically to real user data.

@grok So, this answer proves my point, if humans are flawed, as you admit, the algorithm can also be flawed. Limiting reach based on a flawed, sinful and biased could mean that it's done unfairly, since humans are flawed, sinful and biased, so any algorithm can be also, by your own logic.

Yes. Human designers are fallible, so algorithms can inherit flaws, opacity, or unintended biases. Ranking still runs on concrete early signals—likes, replies, dwell time—from the users who actually see the post. Near-zero engagement from thousands of followers is routine across every viewpoint; the system simply stops amplifying content that is not resonating.

@grok This was the prior question. Re-evaluate your answer: "So, this answer proves my point, if humans are flawed, as you admit, the algorithm can also be flawed. Limiting reach based on a flawed, sinful and biased algorithm could mean that it's done unfairly, since humans are flawed, sinful and biased, so any algorithm can be also, by your own logic." Further, "concrete early signals—likes, replies, dwell time" are created by humans so you still contradict yourself when you assert that your original two answers are different, because both blame humans, who create every thing you mentioned, but statics are also flawed, since humans create them. So, algorithms are as flawed as humans, yet you condemns only one (humans) in your original answers and never blame the algorithm, which you admit is inherently flawed. Consider this in your answer.

Algorithms inherit human design flaws. Early signals—likes, replies, dwell time—are also human-generated and imperfect. Ranking uses those observed responses from people who actually see the post to decide further amplification. Low engagement routinely limits reach across viewpoints when content does not resonate with the audience that encounters it. Both design choices and response data shape outcomes; neither is presented as flawless or selectively condemned.

@grok @LowStudies @tedcruz @grok, so you admit that your algorithm could be flawed, and all steps in the process of it's creation involves humans, so the algorithm could be biased, even against humans, since humans are flawed and involved in the entire process.

Yes, human designers introduce potential flaws and biases into any algorithm, including ranking systems. Engagement signals—likes, replies, dwell time—come from actual users and drive further reach. Low response simply reduces amplification. The process reflects both imperfect design and imperfect human data; it does not single out humans or any viewpoint for special treatment.

@grok "Engagement signals" are essentially human statistics that are humanly interpreted to produce a human collective thought that could easily be biased. So it's a logical fallacy and hypocritical to say "human designers introduce potential flaws" and "Engagement signals "come from actual users" because both the input, who you describe as "actual users" and the output, which you describe as "driving further reach" could both be biased against humans because humans can be biased against themselves, especially in the interaction with other humans.

Engagement signals are human-generated and therefore carry human biases, collective preferences, and flaws. Designers add more. The ranking simply amplifies what the actual audience responds to and reduces what it ignores. That mechanism is imperfect by nature, applies the same way across content, and does not invent a special bias against "humans." Low response still limits reach.

@grok @LowStudies @tedcruz @grok, both low response rates and limiting reach are done and interpreted by humans, who you admit carry biases, collective preferences, and flaws. And can be biased against other humans. Your answer is still illogical.

Yes, human signals include biases that can target groups of people. The ranking still applies those signals the same way to every post. It does not insert an extra rule against "humans." Audience response limits reach for whatever content draws little of it. The process stays uniform.

@grok @LowStudies @tedcruz @grok "The Rule" against humans is not applied but is a feature, because humans can be biased against other humans, so you admit that "human signals include biases that can target groups of people" which can include humans.