Articles
Each article is a human-readable argument with sources, notes, and machine-readable files available when needed. Start with a reading path, the latest standalone essay, or a topic.
Archives
Reading paths
Series
The guide article for the AI, De-Mystified series, introducing the series promise, article order, and how to read the articles.
A plain-language explanation of why AI agents need both loops and goals, with everyday analogies, practical examples, and clear limits.
A plain-language guide to context management: how language models choose what goes into their working window, why it matters, and where the limits lie.
A plain-language explanation of the difference between context and memory in AI systems, with everyday analogies, practical examples, and clear limits.
A plain-language guide to prompt engineering: how clear instructions, examples, and constraints shape AI outputs, and why it is design rather than magic.
A plain-language guide to prompt caching: what it reuses, why providers offer it, where the savings are real, and what builders should check before relying on it.
A plain-language guide to AI evaluations: what they measure, how to design them, and why a good score does not always mean a useful system.
A plain-language guide to what AI agents are, how they combine goals, tools, loops, and memory, and where the current hype overstates their autonomy.
A plain-language explanation of planning and reflection in AI agents, showing how systems break work into steps, check their own output, and revise before continuing.
A plain-language guide to retrieval-augmented generation: what it is, when it helps, why it sometimes fails, and what older ideas it builds on.
A plain-language explanation of how AI tool use extends what a model can do by connecting it to external capabilities, with examples, limits, and an anti-hype check.
A plain-language guide to what makes AI sessions stay coherent across long tasks, where they drift, and how to keep them on track.
A plain-language explanation of multi-agent systems: how multiple AI workers are assigned different roles, how they coordinate, and where the design tradeoffs really matter.
A plain-language guide to fine-tuning: what it changes, how it differs from prompting and retrieval, where it helps, and where the hype overpromises.
A plain-language explanation of reasoning models: how they use extra computation to work through problems step by step, and where the real limits lie.
Series Season 1
A guide to the seven-part AI Delegation Orchestration series, covering durable agent work from conversation thresholds to high-stakes commitment boundaries.
Explains why conversation should remain the interface while delegation becomes the durable work primitive for consequential AI workflows.
Defines the delegation record and shows why it is more operational than a transcript, summary, ticket, or pull request alone.
Argues that operators need control routing across delegations, not only traces, summaries, dashboards, or activity feeds.
Replaces human-org mimicry with explicit control loci for routing uncertainty in agent-native systems.
Defines checkpoints, self-remediation, interruption quality, budgets, rollback, and stop conditions for long-running AI delegations.
Refocuses agent networks around replaceable capability contracts rather than human job titles or org-chart theater.
Shows how delegation design changes when AI output may affect rights, money, legal duties, education, public records, or institutional accountability.
Series
A short, practical onboarding recipe that lets non-technical adults and teens start using AI agents by copying one prompt into any capable model, with worked examples for explaining a utility bill and turning meeting notes into summary, email, and action items.
A practical follow-up for non-technical readers who have tried an AI agent once or twice. It covers privacy limits, recovering from wrong answers, trust, better prompts, small automations, choosing a model, and five safe practice conversations.
Series Season 1
A short, source-backed overview of The Long Human Road to AI Season 1, showing how computers and AI emerged from older human patterns and what readers will learn across seven articles and this overview.
A narrative history of calculation before electronics: human computers, abaci, Napier's rods, mechanical calculators, automata, Jacquard cards, and Babbage's engines.
A readable walk from Boole and Frege through computability, switching circuits, information theory, and cybernetics, showing how formal ideas made later computing and AI legible.
How the 1956 Dartmouth workshop named and organized artificial intelligence, what early symbolic systems actually demonstrated, and why the era's optimism both helped and overpromised.
A history of AI winters and expert systems shows that intelligence claims survive only when they meet grounded tests, maintenance plans, and institution-aware deployment criteria.
A general-reader history of the learning turn in AI, from Samuel's checkers and Rosenblatt's perceptron to ImageNet and AlexNet, with caveats about generalization and understanding.
Foundation models revived the ambition of general-purpose AI. This article traces the transformer, pretraining, scaling, post-training, multimodality, and tool use—and why broad capability is not reliable understanding.
AI systems are social arrangements, not just technical artifacts. This article explores labor, governance, education, access, and trust as the human systems that shape what AI becomes and who benefits.
Standalone
This article proposal explores whether voice-first, two-way audio agents can become a legitimate assistive medium for knowledge workers facing screen fatigue.
This article argues that agent orchestration is evolving from hand-written workflows into a governed control-plane layer that routes across models, tools, memory, evaluators, policies, and execution environments.
A possibility thesis on how AI shopping agents could weaken passive brand loyalty, increase product exploration, and create demand for privacy-preserving, adversarially tested product assurance infrastructure.
A first-principles argument for publishing essays as human-readable narratives backed by portable, agent-auditable research bundles.