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Articles
Technical writing on deterministic verification, Private AI, post-LLM architectures, applied epistemology, and local inference infrastructure.
Meta-knowhow: the new superpower in the age of AI
A professional's value was once measured by what they knew. Today, when AI systems aggregate and refine information faster than any individual, raw knowledge is no longer the differentiator. Controlli...
GPUs are for interpretation. CPUs are for operational knowledge.
Most people think AI infrastructure ends with the model. It doesn't.
Fluency is not reasoning. LLMs need a logic layer.
More than a year ago, I wrote that a serious direction for AI was Transformers + Prolog. Today I would phrase it more precisely: LLMs need a declarative logic execution layer.
Semantics alone is not enough.
A system may connect concepts, organize symbols, and produce highly coherent language. It may relate sleep, stress, glucose, hydration, and behavior in ways that appear meaningful. But semantic cohere...
From Prediction to Admissibility in Medical AI.
The point is not whether a model performs brilliantly on benchmarks, papers, or clinical datasets. The real question is different: what is the epistemic status of its output on the individual patient?
How I Test Vibration and Cooling Before Trusting Expensive AI Hardware.
Almost nobody talks about what happens when you actually turn it on.
My first local inference test lasted 180 seconds.
Four NVIDIA K80 GPUs, GPT-J, standard tower server. At 85°C thermal throttling started. At 90°C, all four cards shut down. Adding aftermarket fans gained about 5°C. Irrelevant.
Epistemic Software Engineering.
The marginal cost of producing software with AI is approaching zero. The cost of knowing whether that software is reliable is not.