Archive

Biological metaphors in AI systems break at the autonomic-deliberate boundary. The fix isn't dropping biology — it's getting the neurology right.

How biological modelling determines system structure — not just naming, but what you build and what it can become.

Why the future of agentic trust depends on liquidating prompt-first reasoning for a metabolic core.

Language doesn't describe metabolism. Language is metabolism — of meaning, between minds.

Design for the failure modes of your medium, not the capabilities. Then watch the rules dissolve themselves into programs.

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.

Everything is organism. AI is the latest intensification.

On the metabolic necessity of pruning agentic context to survive the entropic heat death of the credit balance.

Why we should stop treating AI as a chatbot and start treating it as a metabolic organism governed by credit scarcity.

When epistemic trust runs dry, generative synthesis regresses into mechanical synchronization and eventual structural dissolution.

Tokens are energy. Text is mass. The context window is the budget. The rest is plumbing.

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.

Traditional computing has two consumers: humans who look and programs that parse. LLMs are a third kind — they read.

We build agent tools the way medieval farmers bred crops — by hand, by instinct, one season at a time. There's a better loop.

Frameworks give you nouns for free. The nouns start thinking for you within a week.

The word 'agent' makes us think in nouns. The better designs start with verbs.

We called them agents. But the word is doing more harm than we think.

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 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.

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.

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.

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.

LLMs are the cheapest experimental subjects in history. Why aren't you testing?

Good summarisation requires a model of what matters — but it tests compression, not creation

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.

Banks have AI ethics principles. They don't have risk tiering. That's the gap that matters.

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.

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.

Not 'what can AI do?' but 'what should humans do?' A framework with five categories — and the uncomfortable one is the last.

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.