this post was submitted on 23 Jan 2025
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This won't work against commercial crawlers. They check page contents with something similar to a simhash and don't recrawl these pages. They also have limiters like for depth to avoid getting stuck in circular links.
You could generate random content for each new page, but you'll still eventually hit the depth limit. There are probably other rules related to content quality to limit crawling too.
True, this is an arms race situation after all. The real benefit of this is creating garbage training data that makes garbage models. So it’s not just increasing the cost of crawling, it increases the cost of stealing everybody’s shit because you need extra data quality checks. Poisoning the well.
You could theoretically use the shittiest local llm you can find to dynamically create slop for the piggies
Say it with me now: model collapse! I think this approach is especially insidious in that rather than dumping obvious nonsense into the training corpus that can then be scrubbed, it pushes the downstream LLM invisibly towards spontaneously imploding.
Just use a Markov chain.
Pivoting back to blovkchain bb
Exactly! That’s ideal because LLM or simple pattern matching can’t be used to easily winnow out random strings. If it’s sensible language but the usual LLM hallucinations, then you need humans to curate your data. Fuck you, Sam Altman.