The Journey

Swapping the two LLMs behind an autonomous trading bot from Opus 4.7 to 4.8, live, without it missing a beat. The trick was replaying 78 real prompts first.

More than 80% of AI projects fail, and RAND's leading root cause is not the technology: nobody agreed what problem was being solved. Soft Systems Methodology, built at Lancaster from 1969 for exactly that failure, gives you a way to surface the disagreement in an afternoon instead of a year.

I judge AI models by how often they make me swear. Opus 5 turned swearing into punctuation, and the reason is more interesting than I first thought.

My auth check said healthy for three weeks on a dead credential. The pattern behind it costs more than the bug did.

An audit of 116 instruction files found one stale copy telling twenty repos they were a different project, two files no session has ever read, and 453 lines loaded twice. Here is what to delete from your own CLAUDE.md, and the method that beats judgement.

The most rigorous idealist alive gives the non-duality audience far less than they want: a universal consciousness that is instinctive, has no plan, and is not looking back at you. What he actually claims, why he says it cannot be proven, and the one part that survives whoever turns out to be right.

Seventeen finished pieces of work were invisible to my own tracking system, and the cause wasn't effort or memory. A Berkeley talk gave me the name for what fixed it: an ontology. Here's the version that fits a one-person company.

Last week I showed the demand numbers for AI consultancy are dire. This week a veteran operator explained the part I missed: the demand isn't dead, it's mute. Nobody buys AI. They buy outcomes, told concretely enough to defend to a board.

The YC president gave a talk describing, almost item for item, the AI system I already run. The validation lasted about ten minutes. Then I noticed the word neither of us was saying.

I got phished from my own verified domain, using an API key I'd leaked myself: what actually leaked, how fast it happened, and what I changed.

A friend said it should be a business. I assessed it honestly, decided it was not, and built it anyway because it still needed to exist.

A $1,000-an-hour AI consultancy playbook is doing the rounds. The demand data, and a friend who sells everything except this, disagree.

Spec-first or goal-first? I didn't believe either side, so I built the same tool both ways with one spec and one model. Both worked. The bill didn't tie.

Eleven days of unattended trading. Five losing trades, one winner covered them, +$132 net. What the system did right, what drifted, what I fixed on return.

AI can write working code in minutes. The hard part is trusting it without reading every line. Here's what actually makes AI-built code trustworthy.

An AI agent spent an afternoon optimising one of my apps. It tried a change, measured it, kept the one that was genuinely faster, reverted three that weren...

Six AIs told me Graphify uploads your code. I checked by running it, and the actual gotcha is smaller, quieter, and easy to miss.

Claude has a habit of collapsing an open conversation into three multiple-choice options, and once you see why it does it, you can stop it doing it to you.

A field note from researching Graphify: the real privacy model, the honest token numbers, and why I check the source before I publish.

Same code, same market, three time periods. One made $964, one lost $217, one's up $321. The difference wasn't the algorithm.