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Everything Is Good Now, Which Means Nothing Is: How the Algorithm Abolished Cultural Taste

Everything Is Good Now, Which Means Nothing Is: How the Algorithm Abolished Cultural Taste

Recommendation engines promised to connect everyone with exactly what they'd love — and they delivered. The side effect nobody predicted is that a generation raised on hyper-personalized feeds has lost the shared critical language that once defined what culture even meant. When the algorithm decides what's good, 'taste' becomes a glitch in the system.

The Internet Got Too Big and Broke Into Pieces — and the Pieces Are Somehow Better

The Internet Got Too Big and Broke Into Pieces — and the Pieces Are Somehow Better

The main internet — the one with the algorithms and the brand accounts and the discourse — is becoming genuinely uninhabitable. But somewhere in the wreckage, thousands of tiny, hyperspecific communities are doing something the big platforms forgot was possible: being weird, human, and actually kind of great. This is what happens when the monoculture finally eats itself.

They Built You a Friend and Sent You the Invoice: Inside the Loneliness Industrial Complex

They Built You a Friend and Sent You the Invoice: Inside the Loneliness Industrial Complex

Loneliness in America isn't just a public health crisis anymore — it's a product category. Brands, platforms, and entire business models have quietly reorganized themselves around the specific emotional texture of feeling unseen, and they're very good at making the solution feel personal. The question worth asking is who actually benefits when your sense of isolation becomes someone else's revenue stream.

Everyone Online Sounds Like a Bot Now — And That's Not Entirely the Bots' Fault

Everyone Online Sounds Like a Bot Now — And That's Not Entirely the Bots' Fault

The Dead Internet Theory used to be a fringe Reddit rabbit hole. Now it's basically a documentary. As AI-assisted content floods every platform, something weirder than robot takeover is happening: actual humans are starting to sound indistinguishable from the algorithms trained on them. We're asking what gets lost when weirdness itself becomes a template.