Posts
RSS feed- Choosing a Steam Iron with Vertical Steam
A practical guide to picking a steam iron that handles shirts, steams hanging clothes, and doesn't weigh a ton. Cordless and corded options compared, with Hong Kong Consumer Council test data and current pricing.
- The Organism Theory
Everything is organism. AI is the latest intensification.
- hygiene
On the metabolic necessity of pruning agentic context to survive the entropic heat death of the credit balance.
- LLMs Are Enzymes
Why we should stop treating AI as a chatbot and start treating it as a metabolic organism governed by credit scarcity.
- Conversation Is Metabolism
When epistemic trust runs dry, generative synthesis regresses into mechanical synchronization and eventual structural dissolution.
- Everything Is Energy
Tokens are energy. Text is mass. The context window is the budget. The rest is plumbing.
- Taste Is the Metabolism
Tool descriptions were just the first thing to evolve. Everything in an agent's context window is a genome under selection pressure — and taste decides what counts.
- The Semantic Consumer
Traditional computing has two consumers: humans who look and programs that parse. LLMs are a third kind — they read.
- The Missing Metabolism
We build agent tools the way medieval farmers bred crops — by hand, by instinct, one season at a time. There's a better loop.
- The Vocabulary Trap
Frameworks give you nouns for free. The nouns start thinking for you within a week.
- Design Actions, Not Actors
The word 'agent' makes us think in nouns. The better designs start with verbs.
- The Naming Problem
We called them agents. But the word is doing more harm than we think.
- The Marginal Agent
I deployed twelve AI agents to polish a CV. Five would have been plenty. Here's what the waste taught me about agent team economics.
- The Emergence Ladder: From Molecules to Economies
The larger the system, the less it can be managed and the more it must be emerged. This pattern — from water to ant colonies to AI agents to economies — reveals the design principle for scaling autonomous systems.
- AI Agent Teams Are Colonies, Not Companies
The right organisational metaphor for AI agent teams isn't a company with managers and reports — it's a colony with autonomous workers responding to coordination signals.
- Managing AI Agents Like Managing a Team
The governance patterns for autonomous AI agents are the same ones good managers already use: cadence reviews for normal flow, escalation channels for urgent anomalies, and human judgment only where it has maximum information value.
- Cross-Model Review: Why Model Diversity Beats Model Capability
When AI models review each other's work, independence matters more than intelligence. The same principle that makes external audit valuable makes cross-model review sharper than same-family review.
- Stop Theorizing About Your Prompts
LLMs are the cheapest experimental subjects in history. Why aren't you testing?
- Summarisation Is a Test of Comprehension, Not Intelligence
Good summarisation requires a model of what matters — but it tests compression, not creation
- 270 Agents While I Slept
I ran an autonomous agent loop overnight — 43 waves, ~270 dispatches, ~250 vault files produced. Here's what I learned about building systems that work while you sleep.
- The Risk Tiering Gap in Banking AI
Banks have AI ethics principles. They don't have risk tiering. That's the gap that matters.
- The Unexplainable Alpha
In AI agent systems, execution commoditizes. Research commoditizes. Coordination commoditizes. Taste — the ability to forecast what will matter — is the bottleneck that doesn't automate away.
- The Navigation Problem in Agent Flywheels
Your agent system shouldn't stop when the task list is empty. The real bottleneck isn't execution — it's discovering what's worth doing next.
- Division of Labour: Five Categories for Human-AI Work
Not 'what can AI do?' but 'what should humans do?' A framework with five categories — and the uncomfortable one is the last.
- Programs Over Prompts
The temptation in agent systems is to make everything a prompt. But most of the work is deterministic — and deterministic work deserves code, not suggestions.
- Exoskeleton, Not Colleague
The AI governance conversation is stuck in the wrong frame. The pattern that works isn't autonomous agents — it's exoskeletons. Micro-agents handling narrow tasks, with human judgment at every point that matters.
- The One-Cycle-Late Test
A simple heuristic for deciding how often to review anything: pick the longest interval where being late by one full cycle is still fine.
- Your AI Did the Research. You Didn't.
AI-prepared domain research creates false readiness. The vault says you know five regulatory jurisdictions. You can't name three.
- The TODO Intake Gate
Most TODO systems fail from too many items, not too few. A four-test intake filter for what deserves your attention.
- Match the Tool to the Shape
Not every goal is a flywheel. The most common mistake in personal systems is treating a checklist as something that compounds.