AI Tools

When an artificial intelligence system gives us a wrong answer, almost everyone reaches for the same explanation, which is that the system never learned the thing we asked about. The information was missing from its training, and so the remedy is to supply more of it, whether that means more data, a larger model, or another round of training on our own internal documents. This explanation has become so common that most organizations no longer treat it as an assumption, and they build their budgets and their improvement plans around it. ...

I recently needed to understand a long, cross-departmental process. It was documented across a huge slide deck, a few forms, and half a dozen fields scattered across internal systems. No single source of truth. In my experience, the right tool for this is a checklist. Not a dashboard, not an assistant. Just a simple list of boxes to check in the right order. So I used Claude to read through the materials and produce a tight summary of the process and its steps. I reviewed it, then asked Claude to turn that summary into a checklist draft. I refined it. I’m using it now, and I’ll keep refining it as I go. ...

A few days ago I kicked off several research tasks in parallel and didn’t check which model was running underneath. All of them defaulted to Claude Opus 4.8. When I looked at the bill, I’d spent $350. So the question became obvious. Was the output better? No. I compared the results against similar research I’d run before on cheaper models. No meaningful difference in quality. I still had to verify every source and go deeper on half the areas myself. The expensive model just sounded more confident while doing it. ...

A step by step guide to building a three-agent personal intelligence system that reads your notes, monitors the web, maintains a private wiki, and delivers daily and weekly briefings to your inbox. The thinking behind why I built this is in the companion post: My Personal AI Brain. What you’ll build: A daily email digest grounded in your Obsidian vault and calendar A nightly wiki update that maps your active topics and enriches them with web research A weekly review that closes the week and opens the next one A private, secured website that hosts your living knowledge base Time to set up: 2-3 hours Running cost: $0 (runs on your existing Claude subscription, Resend free tier covers the email volume) Infrastructure: Cowork, Cloudflare (free tier), Obsidian (iCloud sync) ...

Something broke in the last two years. The pace of change in AI alone has reached a frequency that no person can track without a system: new models, new frameworks, new paradigms, new competitive moves. Add to that the normal demands of a senior role: strategy documents, stakeholder alignment, product decisions, customer conversations. Add personal projects. Add family. Add the nagging sense that something important happened while you were handling something else. ...

I have a confession. For the past two years I have been subscribed to more newsletters than I will ever read, following more RSS feeds than I check, and saving more articles than I open. Every morning I sit down with a vague sense that something important happened and I missed it. The problem is not a lack of information. It is too much of it. AI, AI governance, product leadership, agents, MCP, new tools, the space moves fast and the signal-to-noise ratio is brutal. Miss a day and you feel behind. Read everything and you feel buried. There is no good default. ...

Every senior professional I know has the same problem. Too many newsletters. Too many feeds. Too many tabs open. And somehow, despite consuming more information than ever, the feeling that you’re missing the things that actually matter. I built the Personal Executive Briefing Agent to solve this for myself. It runs every night, collects signals from Gmail, RSS feeds, Reddit, and GitHub, scores each item against my actual priorities, and delivers one email every morning with the small number of things that genuinely deserve my attention – and exactly why each one matters to me specifically. ...

In the previous post, I argued that defining decision boundaries is necessary if we want judgment to survive in an AI-accelerated development environment. When execution becomes cheap, the number of decisions explodes. Without clarity around who decides what, speed simply amplifies risk. But defining boundaries alone is not enough. The next question is more practical: how does work actually flow through those boundaries? How does an idea move from a conversation with a customer to something running in production, while AI accelerates the process without collapsing responsibility across roles? ...

The idea of a “second brain” is simple. An external place to put thoughts, notes, ideas, and fragments of work, so your actual brain can focus on thinking and deciding instead of remembering. In theory, this is how knowledge compounds instead of disappearing. I have liked this idea for a long time. It is why I used Microsoft OneNote for years, and later moved to Obsidian. What I never liked was how much work these systems require in order to be useful. ...

A Small Number, a Real Moment CV Match has been out in the world for a few months now. I almost didn’t notice when it crossed 100 installations. That number, by itself, is not impressive. It is small. Almost trivial. What made me stop was not the number, but the realization that this thing actually existed in the hands of people I did not know. Real users, not personas. Real decisions, not demos. ...