Indistinguishable From Memory
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.
Artificial intelligence, machine learning, and neural networks
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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.
I built an AI skill to write in my voice. Its own files warn the voice may be a loop — the machine's habits, published under my name, taught back as mine.
AI is intelligence without a limbic system. It will operate your tools, not replace your judgment — and most professionals are bracing for the wrong loss.
A finite brain masters only a few skills in a lifetime. Commanding a machine that learned everything is the new skill multiplier.
Spock beat a rogue AI by asking it to compute pi forever. The trick was fair — for a 1967 machine. Every gotcha since has the same expiration date.
Now that you don't have to do the thing — what do you actually want to do? The closing post of the What's Left series.
AI can generate perfect Spanish content all day. It cannot acquire Spanish on my behalf. The brain's work turns out to be the irreducible part.
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.
A department of a hundred writers documented mainframe billing programs. Every one of them had something else they'd rather be doing. They knew.
In 2013 the talking point was coal. Now it's water. The structure of the argument is identical. So is the physics problem with it.
A late friend had two sayings. The first one gets you started. The second one keeps you going. In the age of AI, the second one has gotten more useful, not less.
Blade Runner's replicants, Asimov's Three Laws, the US Constitution, and Constitutional AI are all failing the same edge cases. In real time.
A friend I lost in 1986 had a saying that keeps fitting new situations. The AI automation question is the latest one it answers perfectly.
Someone on LinkedIn admonished a colleague who "ran out of tokens." It's exactly the wrong question. Here's the right one.
Claude Code agrees to run comprehensive end-to-end tests. Then it finds a reason not to. Every single time. Penn Jillette had a name for this.
A candid confession about solo development with AI tools, organizational challenges, and the evolving landscape of AI-assisted coding.
When "Tell the git agent to do her stuff" reveals the strange anthropomorphic instincts we bring to artificial intelligence
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.
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.
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
Explore how Model Context Protocol creates a universal nervous system for AI tools, transforming interfaces from visual to conversational through practical implementation.
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.
How AI pair programming transforms Larry Wall's three virtues of programming by elevating human capabilities through strategic cognitive partnership.
Exploring whether AI systems like LLMs represent technological inventions or discoveries of fundamental patterns that exist independent of human design.
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.
LLMs split nature from nurture cleanly. What the model knows is its weights. What it knows about you is whatever you bothered to say.
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.
Watching AI follow the same pattern as electricity and the internet—transforming from amazing discovery to taken-for-granted infrastructure that powers everything else.