personal-systems
8 essays on this topic.
- Try Widely, Build Narrowly
Whether to use language models for every part of life is the wrong question. Trying one costs nothing and is how you find the tasks that did not exist; building around one is what costs, and the bill arrives as maintenance.
- The knowledge base is the maintenance
A working thesis, six months after my LLM wiki piece: plain Markdown plus relentless truth-keeping beats sophisticated storage plus neglect.
- The Skill Is Knowing What Matters
The bottleneck in a world of AI tools isn't crafting the output — it's knowing which output is worth crafting.
- Act-on-Receipt: The Third Task Class
Most task systems are binary, but a third class exists — tasks triggered by external notifications — and managing them like a backlog item is the wrong move entirely.
- Push Not Pull
AI agents that require you to go looking for their results aren't agents — they're automation with better UX. The loop closes when results arrive, not when you remember to check.
- The Identification Problem
Having great AI delegation tools and not using them isn't a tool problem — it's a pattern recognition problem, and that distinction changes everything.
- The Last 10% Is the Feedback Loop
The execution layer of an AI system is only half the infrastructure — the reporting layer is what determines whether anyone acts on the results.
- The Deliberation Format Is the Product
I ran an experiment to find where multi-model deliberation adds value. The answer surprised me: it's the structured format, not the model diversity.