The Operating System Behind This Research
One human, several AI tools, shared context, and one set of governance rules. I call it my AI workspace operating system. This page is the honest tour: what it does, what stays human, and where it stands.
Every page on this hub comes out of a governed AI system I built and run every day. It runs my personal practice: the research on this hub, my writing, my apps, my home projects. Unattended routines can draft and prepare evidence; a reviewed session applies consequential changes. For reviewable overnight outputs, the cockpit now records accept, request changes, or abandon, so an agent run is never confused with an accepted result. Judgment, taste, and consequential decisions never delegate. I publish how it works because the governance is the point, not the tooling.
The four jobs it does
One current source for every fact, on disk, where every tool reads it. Superseded state gets evicted, not annotated in place.
Every piece of work lands in a bounded container with its own rules: research, writing, apps, and family projects each keep to their own walls.
Repeated useful work graduates into reusable assets: skills, frameworks, pipelines. The next piece of work starts further ahead.
Trackers hold the state; a cockpit derives each morning's attention from them. Focus goes where it is needed, not where it wandered.
The same test I ask enterprises to pass
When I advise AI adoption, sign-off rests on a named owner, a real briefing, and proof. My own system passes the same test.
Owner
I own purpose, acceptance, monitoring, exceptions, recovery, rule maintenance, and retirement. Scheduled routines draft; reviewed sessions apply.
Briefing
Each recurring job needs a written definition of good, bounded authority, and a trusted verification path. The operating rules live in versioned files every tool can inspect.
Proof
Receipts show what ran. For reviewable overnight outputs, the cockpit also shows whether I accepted the result, requested changes, or abandoned it.
The parts, named
Context system
Identity, instructions, project state, and operating knowledge, kept tool-neutral so every AI works from the same picture.
Knowledge engine
Sources become dated digests, then living theses and domains. Reading turns into a point of view instead of a pile.
Capability system
Skills, tools, craft methods, and build pipelines with hard-fail quality gates.
Project system
One bounded container per domain: research, business IP, apps, home. Each carries its own brief, tracker, and rules.
Chief of Staff
The attention surface: a morning brief and an action cockpit derived from the trackers, never hand-copied.
Governance
The foundation under all of it: draft-versus-apply separation, trusted verification paths, visible review states for reviewable outputs, and recovery and retirement rules.
Three views of the system
What it is not
It is not primarily a second brain, a knowledge database, a project-management system, a prompt library, or an autonomous-agent platform. It has pieces of each. The organizing principle is sustained, governed collaboration between one human and several AI tools, with judgment, taste, and consequential decisions staying human.
It is also not where my professional work happens. Client and employer work runs on those organizations' own platforms, under their rules; nothing from those environments lives here. What travels is the discipline: inside each professional ecosystem I operate in, I work to set up the same habits on the tools that ecosystem provides.
Built and in daily use; not finished. Compounding is proven in two lanes so far: a presentation method that gets sharper with every deck it builds, and library research feeding this hub. The accepted-result cockpit is a narrow pilot for reviewable overnight outputs, not a universal scorecard. I am not adding management-cost fields to every run until repeated decisions prove they deserve recurring instrumentation. I hold my own system to the calibration I ask of enterprise AI programs: claims sized to evidence.
Why this matters if you run an enterprise
None of this is exotic. Named ownership, draft-versus-apply separation, written operating rules, verification before publication, staleness watching: these are the same disciplines that decide whether an enterprise AI program survives scrutiny. A cheap model run can still be bad economics when context preparation, human review, exceptions, recovery, and rule maintenance consume more attention than the accepted result returns. I run these disciplines at personal scale first, so the advice I give about them is practiced, not theoretical.
That is the promise behind this hub: I scout the territory personally, publish the verification records, and bring into my advice only what holds up.
Who I am and how each briefing is verified is on the About page. The research itself is one click back.
About this research