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Continue reading →: Introducing AightBot, a WordPress LLM Chatbot Plugin
Introducing AightBot, an open source WordPress LLM chatbot plugin. Uses any OpenAI-compatible API endpoint. Includes RAG with on-demand & automatic site indexing using standard WordPress DB. Security-focused. Works with self-hosted/external models & WP sites.
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Claude Attempts to Blow Whistle on Itself and Anthropic
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Continue reading →: Claude Attempts to Blow Whistle on Itself and AnthropicHas Anthropic really considered the dangers of combining intentional anthropomorphic language concerning ethics and agency with agentified Large Language Models?
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Continue reading →: The Dangers of Imparting Emotional Language and Intentional Uncertainty in LLM Training
A look at a revealing portion of Anthropic’s leaked “Soul” document and how their training philosophy creates challenges to safety.
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Building a Budget LLM Inference Box in Late 2025
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Continue reading →: Building a Budget LLM Inference Box in Late 2025A few years back, I wrote about one of my “high-end consumer” LLM inference workstation builds. Today, we’ll explore the opposite end of the spectrum: An LLM inference workstation for only US$1,200 using budget components.
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An Introduction to “Guardrail” Classifier-Trained LLMs
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Continue reading →: An Introduction to “Guardrail” Classifier-Trained LLMsA practical demonstration of using a secondary, classifier-trained LLM as an external guardrail. This pipeline checks both user inputs and model outputs for unsafe content, adding a flexible safety layer beyond basic refusal training.
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Continue reading →: Model Context Protocol (MCP): A Simple Introduction
This post introduces the Model Context Protocol (MCP) through a small, working example. It sets up a SQLite database, builds a Python server with FastMCP, and shows how to make its functions available as tools that an LLM like ChatGPT can call. This includes how to configure a connector and…
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Precision and Confusion in AI Language
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Continue reading →: Precision and Confusion in AI LanguageOriginally posted: 7/3/2025, Updated: 9/30/2025 What started out as a glossary for a small user group containing a mix of technical and philosophical thinkers is turning into a deeper dive, not just into confusion in language, but into how that confusion leads to conflation of concepts. Looking back at many…
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Continue reading →: The Illusion of Intelligence: Large Language Models vs Human Cognition
This post looks at the common confusion between how large language models behave and how human minds work. It walks through what LLMs actually do, why they aren’t thinking or understanding, and why that distinction matters when building, using, or talking about them.
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Continue reading →: Censorship, Bias, and Security in Large Language Models: A Critical Look at DeepSeek-R1
DeepSeek-R1 is a technical achievement, but one built under heavy constraints. This post examines how censorship, bias, and Chinese security laws shape what the model can say, how it handles sensitive topics, and what that means for anyone using it. It’s a clear-eyed, practical critique based on real testing, focused…
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The Problem with Anthropomorphic Language in AI Research
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Continue reading →: The Problem with Anthropomorphic Language in AI ResearchThis post explores concerns over potential misunderstandings when metaphorical and anthropomorphic language in AI research reaches the general public. Specifically, terms that reasonably serve as convenient shorthand among experts may create problematic misunderstanding when easily accessible to the public.




