Archive
AI controls, agent systems, banking governance, production practice. 409 essays, newest first.
- AI Controls Architecture
Risk teams know risk. The open problem is designing controls for systems that are non-deterministic, probabilistic, and attackable in natural language.
- Governing Agents the Way Cells Govern Themselves
Six cell biology mechanisms that reveal what the networking 'control plane' metaphor misses about governing AI agents.
- The Risk Without an Engineering Solution
Every other agentic AI risk has an engineering answer. Prompt injection doesn't. That changes everything about how you design controls.
- From Chatbots to Event Loops
The shift from agents you summon to agents that watch. Enterprise AI workflows are becoming continuous loops — and the failure modes are different.
- What MCP Actually Changes for Enterprise AI
Not better function calling — decoupling. When tools expose MCP servers, any agent can compose any system freely. The heterogeneity problem becomes a configuration problem.
- Language Is the Medium, Not the Purpose
We called them language models and spent years confused about why they could reason. The name stuck to the interface, not the mechanism.
- LLMs Are Better at Editing Than Writing
Ask an AI to write from scratch and you get the average of the corpus. Give it something rough and it amplifies what's already there. The workflow implications are significant.
- Consulting Is Mostly About Reducing Uncertainty
Clients hire consultants to solve problems. What they're actually paying for is the reduction of a particular feeling. The distinction matters.
- The Case Against Knowledge Management Systems
Most PKM tools are procrastination with better aesthetics. The problem isn't the software — it's that filing a note feels like understanding it.
- What It Actually Feels Like to Use AI for 80% of Your Work
Not productivity. Something stranger — the cognitive texture of days when the bottleneck shifts from execution to articulation.
- When the Platform Is Mature, the Architect's Job Changes
The hardest phase of AI architecture isn't building the stack. It's the moment after the stack is built and eighteen teams start making independent decisions on top of it.
- The Calibration Trap
The comfort trap is about effort. This one is about epistemics — and it's harder to see.
- The Comfort Trap
The right test for any AI interaction isn't 'did it help me?' but 'am I more capable after it?'
- The Personalised System Era
AI coding agents didn't just make developers faster. They changed who gets to have a bespoke system.
- Let the OS Schedule, Let Your Tool Dispatch
The moment I stopped building scheduling into my tools, everything got simpler.
- The Nag Tax
When building automation around a third-party app, the first question to answer is: what's the one thing this app does that nothing else can replicate? That feature becomes the tax you pay on everything else.
- Benchmark Your Research Stack
Running 10 real queries through 5 tools revealed that theoretical routing rules have systematic gaps — and the surprises were more useful than the confirmations.
- The Queue Should Live Where Your Thoughts Live
AI agent results should be push, not pull. The feedback loop should close on mobile. Most tools miss all three — not from ignorance, but because dashboards photograph better.
- The Infra Trap
Building tools to support your work can quietly become a substitute for the work itself.
- The Queue That Texts You Back
Personal AI infrastructure should report results to you, not wait for you to go looking. A small architecture shift changes the whole dynamic.
- Eliminate the Reminder, Don't Schedule It
When you catch yourself setting a reminder to check something later, that's usually a signal that a tool is failing to report what it should.
- Where Rules Live
The difference between a rule that works and a rule that doesn't is usually not the content of the rule — it's where it lives.
- When Better Is Worse
Upgrading to a more capable model made my tool sixty times slower. The lesson isn't about models — it's about the difference between capability and fit.
- The QDAP annuity trap: what the tax saving doesn't tell you
Hong Kong's QDAP annuity is sold on a real tax benefit. But the HK$60K deduction cap is shared with MPF top-ups — and that changes everything.
- The Experiment Loop Without the GPU
Andrej Karpathy's autoresearch project is being read as a demo of what H100s can do overnight. It's actually a discipline for doing rigorous work on anything measurable.
- Instructions Don't Enforce Behavior. Templates Do.
Why the structure of an output matters more than the instructions that produce it.
- The Silent Stall: Debugging GPT-5.4-Pro's Responses API
Three hours of debugging revealed two non-obvious behaviours about GPT-5.4-Pro that aren't in the docs: a minimum token budget requirement and a wall-clock timeout gap in Rust async code.
- I Didn't Mean to Kill My Todo App
A coding assistant quietly made three productivity apps redundant. Not by replacing them — by making context collapse the boundaries between them.
- What it actually takes to run an AI agent in a bank
The resistance to AI agents in banking isn't mostly cultural. It's infrastructure — and the gap is more interesting than the politics.
- AI Fixed My Perfectionism (Sort Of)
On why the blank page stopped being the hard part.
- Exa Indexes WeChat
WeChat is supposed to be a walled garden. Exa didn't get the memo.
- The Problem With Clever Browser Automation
The most sophisticated solution to a problem is usually a sign you haven't found the right abstraction yet.