Posts

The people who examine the system they're inside tend to make better decisions within it.

After a decade in financial services, I've stopped believing that what you earn reflects what you contribute.

I use AI every day. I genuinely can't tell if it understands anything. That question is harder than it looks.

I work in AI in financial services. The honest list of where gen AI is real is shorter than the industry wants you to think.

Supporting a family in a system you see clearly isn't selling out. It's the most honest position there is.

My career looks like a plan in retrospect. It wasn't. It was a series of pushes, wrong calls, and adjustments.

The most successful piece of propaganda in modern economics is the idea that what you earn is what you deserve.

People confuse the LLM with the helpful AI assistant. They're not the same thing. The LLM is a prediction engine. The assistant is a role it's playing. The distinction changes how you use it.

An LLM can't feel accountability pressure. But structured role-play — simulated rejection, persona assignment, adversarial review — produces measurably better output. The mechanism is opaque; the effect is real.

AI's first 'I'm done' is almost never its best work. Simulated accountability pressure — just asking 'are you sure?' — surfaces blind spots that self-review misses.

I ran the same question through 6 AI tools and scored them against peer-reviewed evidence. Every tool got something wrong that another got right.

Nobody practises arithmetic speed anymore. The same thing is happening to prose, research, and analysis — and it changes what humans should get good at.

Naval's famous line is easy to nod at. The hard part is actually identifying yours — and being honest about what isn't.

30 days of Oura data showed activity type doesn't predict stress. Meetings do.

From AppleScript's rigid English-like syntax to LLM tool-calling — what changes when the computer actually understands you.

Automation that collects without learning is just a cron job. The difference is a feedback signal — a number that goes up or down.

Karpathy's autoresearch and every useful AI tool share the same pattern: the code is trivial, the feedback loop is the product.

You need the tool to build the tool. The answer is: build the dumb version first, use it once, then have it build its replacement.

Every AI lab builds single-vendor orchestration. The cross-vendor layer is a gap — and it's a gap for a reason.

Multi-agent AI orchestration frameworks are commodity. The competitive advantage is knowing which agent to use when, what breaks, and how to recover.

When pattern-matching feels like wisdom, what are we actually experiencing?

Ethics isn't about knowing the answer — it's about feeling the tension

The question I can't fully answer for my son

When AI conversations feel insightful because the language model is good at producing insight-shaped text

The things that matter most in production are the things that get benchmarked least

When the paper becomes more interesting than the answer you set out to find

The missing layer in enterprise AI isn't smarter models — it's structured memory that humans can actually review.

Prompt instructions are suggestions. Hooks are constraints. One survives a model swap.

The 'ship and calibrate' loop works beautifully for reversible decisions. For the big ones, you're mostly guessing and then making the guess true.

AI commoditises execution. What remains is taste — the 'that's the one' reflex. And the only way to sharpen it is to ship and see what reality says back.