Introductory deep-dives into AI concepts
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A line in a stranger's bio sent me into a conversation about Hegel and Kant. The plain-English answer was accurate and I still couldn't follow it. The fix is the same one that teaches languages.
A model remembers nothing between sessions and cannot verify a word you say. The published rulebooks say more than the behavior does, and most workarounds are boring.
A large language model only ever produces text. Treating that text as a novel ethical category, rather than as speech, is where the argument goes wrong.
Four things to write down before you hand a job to an AI agent: what comes in, the rules, the action, and how you will check the work.
A token is a LEGO brick with a number on it, and almost none of the math that moves it is new.
A thought experiment exploring what happens when AI learns from narratives that prioritize meaning over facts - and the crucial difference between metaphor and false belief.
Edison metered electrons by the ampere-hour before anyone metered tokens, and the War of Currents was fought over the same problem the AI economy has now: getting a dead commodity to a device built to do something with it.
A taxonomist's view of Jev: the closed-category, confidence-scored work I built by hand for AT&T in the 90s, now manufactured to order by an LLM.
The man who coined 'artificial intelligence' invented the most elegant language ever designed for it. It was the wrong tool — because it was the wrong category.
Exploring whether AI systems like LLMs represent technological inventions or discoveries of fundamental patterns that exist independent of human design.
LLMs are stateless. Every fact you give the model is re-fed as text each turn, then forgotten — the same weights answer a stranger's steak question a millisecond later.
Lessons from 90s tech predictions on navigating AI's future optimistically, examining how dystopian predictions consistently fail while protopian thinking accurately maps our future.
How to transform dangerous electrical potential into reliable, controlled intelligence through proven grounding techniques. LLMs are like ungrounded electrical circuits—full of dangerous potential.
How modern AI language models accidentally rediscovered what linguists have known for decades about language acquisition through comprehensible input.
Recent LLM rollbacks highlight a growing concern: our AI systems are becoming dangerously agreeable, praising even obviously flawed ideas. This pattern mirrors broader societal issues around sycophanc
Exploring how modern AI systems mirror the dual-processing architecture of human cognition as described by Daniel Kahneman's "Thinking Fast and Slow" framework.
How Star Trek's ESP concepts anticipated our current reality where AI models transform multidimensional data into human-perceptible insights.
Explore how Model Context Protocol creates a universal nervous system for AI tools, transforming interfaces from visual to conversational through practical implementation.
Organizations struggle with digital debris (ROT data) that wastes resources and creates liability. Modern AI systems with tool access through protocols like MCP provide a solution by enabling governan
Despite remarkable advances in AI and other technologies, fundamental digital infrastructure problems like secure email, calendar coordination, payment systems, and tax filing remain unresolved due to
How AI agents can fail spectacularly by missing the point entirely while technically fulfilling requests—and why this matters for AI development.
Explore how dimensional transformations shape the economics of truth, from the computational abundance of LLMs to the persistent scarcity of validation in our information ecosystem.
Exploring uncomfortable parallels between biblical guidelines on slavery and our modern relationship with AI language models.
Discover how AI project spaces leverage Deming's PDCA cycle and root cause analysis to transform manufacturing troubleshooting without complex integrations.
Exploring the parallels between Data General's microcode heroics and today's AI revolution, questioning what defines consciousness in both humans and machines.
Explore how AI-powered 'vibe coding' fits into the historical progression of programming abstractions and why it represents the natural evolution of software development.
Examine how dystopian tech predictions consistently fail while protopian thinking accurately maps our future, from 1990s internet debates to today's AI discourse.
Explore how information theory reveals fundamental patterns that connect everything from neural networks to cosmic structures, suggesting a universe built on interconnected nodes.
The Perl book lists three virtues of a programmer that look like vices. Once the machine does the typing, you have to supply them yourself.
How the hidden dimensions of AI systems mirror human cognition. Discover the multidimensional mathematical spaces where machine understanding happens—and why they matter for both silicon and carbon mi
Explore the striking contrast between Neuro-Linguistic Programming and Natural Language Processing—two fields sharing an acronym but diverging wildly in methods, evidence, and results.
An exploration of how AI technologies are inevitably confronting our most sensitive social domains, from politics and religion to healthcare and wealth distribution.
How a simple blog naming session became a masterclass in AI-human creative partnership, resulting in the invention of CIqSi - a fictional element that bridges carbon and silicon intelligence.
LLMs split nature from nurture cleanly. What the model knows is its weights. What it knows about you is whatever you bothered to say.
Watching AI follow the same pattern as electricity and the internet—transforming from amazing discovery to taken-for-granted infrastructure that powers everything else.