AI

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.

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

Same spec, same model, two methods: a seven-part goal prompt against a full agent framework. The lean prompt tied on correctness and won on time, cost, and clutter. Here are the numbers.

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.

How a trading agent reveals the game retail can actually win — by playing where the funds can't or won't go, and learning to love the play.

I checked the XML files BOS-AI used to store 'institutional memory' for thirty agents. Every tag was PLACEHOLDER_X. So I deleted all twelve.

I shipped v6 of agent-11 last month. The biggest single change was deletion. The deployed CLAUDE.md went from roughly 250 lines to under 80. The MCP prof...

Seven months of building bought me a system that finally tells the truth about itself. The data those seven months produced is mostly unreadable. The clock just started, and not a moment too soon.

Three forces are converging on retail crypto traders, and none of them favour the small player. The edge isn't intelligence anymore. It's owning the table.