SoyViking [he/him]

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Joined 5 years ago
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Cake day: November 4th, 2020

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  • Back in the day, there would be a long counter in shops with a clerk in a brown apron behind it. You had to ask him for the goods you wanted. This made shoplifting very hard. If they are pissing themselves over a stolen candy bar they would have to go back to this model.

    But there is a reason they stopped doing it that way. Self-service shops might experience more loss but the saved labour costs and increased sales makes up for that many times over.

    These wankers want to have their cake and eat it to. They want all the earnings from self-service with none of the downsides.







  • We have, unambiguously, the most pro-trans major/recently-ruling political party in the world. The Democrats are literally as good as it gets, in terms of major-party support, anywhere on earth for us. Everyone will get mad at me for saying it, but it’s a fact.

    The Communist Party of Cuba literally exists and have passed the world’s most progressive family law code.

    Besides Cuba, many places around the world, from Sweden to Iran, has trans healthcare as part of their universal healthcare systems. The US democrats are vehemently opposed to even the idea of universal healthcare, let alone transgender care.

    Americans doesn’t know shit about anything that happens in the rest of the world.







  • I ran the article through ChatGPT five times. It should be super-improved by now:

    CW: AI slop

    Here is a refined version that preserves your argument while tightening cadence, sharpening conceptual clarity, and reducing minor redundancies:


    Semantic Ablation: Why AI Writing Is Boring — and Potentially Dangerous

    The AI community coined hallucination to describe additive error — moments when a model fabricates what was never present. We lack a parallel term for its quieter, more insidious opposite: semantic ablation.

    Semantic ablation is the algorithmic erosion of high-entropy meaning. It is not a malfunction but a structural consequence of probabilistic decoding and reinforcement learning from human feedback (RLHF). Where hallucination invents, semantic ablation subtracts. It removes precisely what carries the greatest informational weight.

    In the act of “refinement,” a model gravitates toward the statistical center of its distribution. Rare, high-precision tokens — those inhabiting the long tail — are replaced with safer, more probable alternatives. Safety and helpfulness tuning intensify this centripetal pull, penalizing friction and rewarding fluency. The result is not falsehood but attenuation: low perplexity purchased at the cost of semantic density.

    When an author asks AI to “polish” a draft, the apparent improvement is often compression. High-entropy clusters — loci of originality, tension, or conceptual risk — are smoothed into statistically reliable phrasing. A jagged Romanesque vault becomes a polished Baroque façade of molded plastic: immaculate in finish, hollow in load-bearing strength. The surface gleams; the structure no longer carries weight.

    Semantic ablation can be understood as entropy decay. Pass a text through successive AI refinements and its informational variance contracts. Vocabulary diversity narrows. Type–token ratios decline. Syntactic range constricts. The process typically unfolds in three stages:


    Stage I: Metaphoric Cleansing

    Unconventional metaphors and vivid imagery deviate from distributional norms and are treated as noise. They are replaced with familiar constructions. Emotional friction is sterilized.

    Stage II: Lexical Flattening

    Specialized terminology and high-precision diction yield to common synonyms in the name of accessibility. A one-in-ten-thousand word becomes a one-in-one-hundred substitute. Semantic mass diminishes; specificity thins.

    Stage III: Structural Convergence

    Nonlinear reasoning and idiosyncratic argumentative architecture are coerced into predictable templates. Subtext is over-explained or erased. Ambiguity is prematurely resolved. The prose becomes syntactically impeccable yet intellectually inert.


    The finished product resembles a JPEG of thought: coherent at a glance, depth stripped away by compression.

    If hallucination is the model perceiving what does not exist, semantic ablation is the model erasing what does. The danger is not merely aesthetic monotony but epistemic smoothing. As refinement is outsourced to systems optimized for statistical centrality, discourse drifts toward the median. Originality becomes an outlier. Complexity dissolves into algorithmic smoothness.

    If we fail to name this process, we risk acclimating to it. And once acclimated, we may forget what uncompressed thought feels like.