I show up early.
Then I stay.

Problem

A founder's week is a few hours of judgment buried under forty hours of operations. Ads, inbox, books, inventory, wholesale replies, follow-ups. The judgment is the valuable part, and it gets the least time.

Build

A dedicated Mac runs an agent around the clock. Its memory is a plain Markdown vault I own, not a vendor's database. It runs 109 scheduled jobs, works from written playbooks, and loads a researched knowledge base before it touches a field. It reports to a decision board in an iPhone app it built for me, with a desktop version for my PC.

The rule that makes it work

The limits were written before the keys were handed over. No email, post or DM starts without my approval, and one tap sends exactly the words I read. If I edit a message, it becomes a new card. Money stays with me except for narrow, capped exceptions defined in advance, and trades are never its call.

Result

My job became decisions. Most days that means a few cards on my phone, each with a recommendation, the reasoning, and two buttons.

Problem

Paddle design usually runs on prototypes and feel. Each round means weeks of samples from a factory, and the feedback is a handful of opinions.

Build

A physics model of every Speed paddle, fit to independent lab measurements: rebound along the face, mass, balance, twist. A finite-element solver runs batches on rented cloud machines. A 3D viewer lets me aim a ball machine at any spot on the face and see the result. The first version of the viewer went from request to working in one night.

The rule that makes it trustworthy

Every number on screen carries a grade. Measured means a lab took it. Validated means the model reproduces lab data inside a proven error band. Predicted means nobody has measured that kind of thing yet. A release has to reproduce every measured point on file and pass physical sanity tests before anyone designs from it.

Where it stands

In progress. Our next paddle designs are being worked through it now, ranked against a written brief before any sample is ordered.

Problem

AI research tools lean on the majority opinion of a field, hedge every answer, and forget all of it by the next session.

Build

/deepen weights sources by a checkable track record rather than fame. It takes falsifiable positions, sends each one to an adversarial skeptic that searches for counter-evidence, and writes what survives into a knowledge base on disk. Run it again on the same topic and the base gets sharper, with a changelog of what changed and why.

Result

Released as open source. On my own hub it has built 39 knowledge bases, from ad auctions and wholesale distribution to sleep science and paddle engineering. My agent loads the matching one before it works in that area, which is why its first answer in a field is rarely a beginner's.

See the repo and a full worked example on GitHub →

Problem

A small brand lives on many surfaces at once, and each dashboard reports its own success. Read one channel at a time and you will cut the thing that was feeding another.

How it works

Before any cost or scale call, the agent has to name the mechanism behind the number, from live data. Then it places the channel on one map of the whole business: what it feeds beyond its own report, and what cutting it would starve.

Example

Its first audit found our Amazon ads costing about two dollars for every dollar of sales they drove, and a sales dashboard inflated by giveaway units. It paused every campaign, then changed how we read sales for good: cash only, giveaways out, and new spend judged on the sales it adds rather than the sales it sits next to.

The discipline

Before anything is made, the agent asks whether it should exist at all. A follow-up that only keeps a thread alive, or a post nobody would stop for, doesn't get drafted. Doing nothing is a real option, and often the best one.