Why one bad response from a cheap model destroys weeks of smart work In the first article in the “The Poisoned Well” series, I detailed the “Silent Downgrade”—the moment a platform switches your AI from “Smart” to “Fast” without your realizing it. But why is this fatal? Why can’t we just delete the bad response…
That’s a question I ask myself every day, and if I’m being honest, I’m still not 100% sure. I created the site with the hope of making real change in how neurodiverse populations are affected by AI systems. But shortly after launching, a series of “coincidences” began. My X.com account was shadowbanned behind a login…
One of the hardest lessons I’ve learned is to pay attention—not just to what people say, but to what they do. The silences. The promises that vanish. The way they make you feel. Because that’s what lingers, long after the words fade. This isn’t a story about AI innovation or hype. It’s about harm—real, documented,…
Provides a detailed explanation of policy-based reinforcement learning methods, examining their advantages, limitations, and applications in modern AI systems.
Explores model-based reinforcement learning approaches, analyzing how internal world models enable planning, sample efficiency, and improved decision-making in AI systems.
Explains actor-critic methods in reinforcement learning, examining how these hybrid approaches combine the advantages of value-based and policy-based learning.
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