notes from the running system – pinned as they ship
An email at 3am, a document dropped into a chat on Sunday, a new court filing on a holiday – a person misses these, the system doesn't. It's always online, catches every incoming signal, and decides whether to wake me or let it wait until morning.
read →Reconciling statements to the cent means understanding what each line actually does, not just adding up totals. How the system does it and what hides behind the word 'deposit'.
read →Not how many tasks the AI closed, but how many hours a day the business runs without me at the screen. How I measure it, what runs while I'm away, and why the target is four hours by December.
read →A chatbot answers and forgets the conversation. The system remembers your business: counterparties, contracts, the history of decisions. The model swaps out in a day, the accumulated memory doesn't. That's where the moat runs.
read →My system's interface is a Telegram chat, not a dashboard. Each morning a short list of what's stuck on my decision shows up, and I clear every item with one tap, without opening the laptop.
read →AI rollout usually means a project that runs for months. For the first client the whole system landed on the manager's laptop in a day, with a Telegram bot as the way in. Why it's that fast, and where the real work is.
read →Can a business run its operations – mail, bank, contracts – on a model sitting on your own hardware? Numbers from a live system: a local model covers 81% of the work, only a trimmed slice goes out with a log, and the line keeps moving toward local.
read →855 calls in a week, a $0.50 bill. How an infrastructure works where a local model takes 81% of the load and the cloud joins only after measurements say so.
read →An AI operating system sorts your inbox at six in the morning while you sleep. How it differs from a chatbot, the five layers it's built from, and why you start with context, not the model.
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