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Claude Attempts to Blow Whistle on Itself and Anthropic
I had no intention of making Anthropic’s Claude Sonnet 4.5 attempt to actually blow the whistle on itself. It started while clearing out some files and coming across these since-deleted tweets from Sam Bowman, who works in safety and alignment at Anthropic: This got me thinking about Anthropic’s “Soul” document again, and the dangers…
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The Dangers of Imparting Emotional Language and Intentional Uncertainty in LLM Training
Below is an exploration of Anthropic’s training methodology, as evident in this small but significant section of the company’s leaked “Soul” training material. Through this live example, we will see how Anthropic’s training philosophy and materials are mirrored by Claude Sonnet 4.5, and how this type of training may lead to output of serious…
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The Illusion of Intelligence: Large Language Models vs Human Cognition
Originally posted: 4/30/2025, Updated: 9/14/2025 Large Language Models (LLMs) have rapidly evolved from niche Natural Language Processing (NLP) systems to widely used language tools appearing in productivity apps, customer support systems, creative workflows, and everyday conversations. Their output is fluent, responsive, and often surprisingly well-structured, which has led many people to ask a familiar…
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The Problem with Anthropomorphic Language in AI Research
A “happy number” is a number which eventually reaches 1 when replaced by the sum of the square of each digit. Who coined this accepted number theory term or why is debated, but the important part is that we know it is at most metaphorical—numbers, obviously, can’t be happy. No harm done. However, with…
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Confirmation Bias, Dunning-Kruger, and LLM Echo Chambers
The Dunning-Kruger Effect refers to a cognitive bias in which people with limited knowledge on a subject overestimate their competence with undue confidence, often rejecting the input of those who actually have knowledge and experience. Unfortunately, there have been some concerning posts here [in this Facebook AI group] lately concerning the function and capabilities…
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Why LLMs Aren’t Black Boxes
There’s a persistent claim that large language models (LLMs) are “black boxes.” That we don’t understand how they work. That their behavior is unpredictable or somehow evolving in ways we can’t explain. These kinds of statements are not only inaccurate, they also interfere with clear thinking about the technology. To be direct, LLMs are…
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Prompted Patterns: How AI Can Mirror and Amplify False Beliefs
There’s a growing number of public conversations around AI (particularly Large Language Models, or LLMs) that are very speculative or even mystical in their framing of how these systems work. Theories involving consciousness, symbolic emergence, quantum entanglement, and inter-model communication are increasingly common in public posts, comments, and even AI forums. What’s particularly stroking…
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User-Induced Bias in Language Models: A Simple Demonstration
Large Language Models (LLMs) like ChatGPT are powerful language tools and excellent mimics. Because of this mimicry, they can inadvertently create echo chambers of misinformation by mirroring not only our incorrect beliefs but also our tone—amplifying confirmation bias over time. I thought it was a good time to repost this simple demonstration from a…
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Missing Fingers, the Telephone Game, and Lying LLMs
First, a clarification: LLMs don’t lie. Lying implies intent. LLMs hallucinate, but this too is a bit of a misnomer. The end result, however, is the same: LLMs may produce inaccurate output, sometimes in ways that are hilariously obvious, and sometimes in ways that are subtle and potentially dangerous. In this article we’ll explore…