Full disclosure up front: I am a founder, and this is about something I built and open-sourced. If that is not your thing, no hard feelings.

The problem was never the model

I run a small software company. Most of my day is a pile of small, different jobs: marketing, the website, a sales follow-up, a decision about a product. I started leaning on AI chat for all of it, and the model was genuinely good. That was never the problem.

The problem was that every new chat started from zero. I would re-explain the same context, the same positioning, the same constraints, over and over. Decisions I had made last week evaporated. The AI was smart, but it had amnesia, and I was the external hard drive.

Why I did not reach for an autonomous agent

The obvious 2026 answer is "use an agent." There is a whole wave of them now, the kind that go off and do things on their own. I looked, and honestly I did not trust handing over actions I could not see or verify. I did not want something acting on my company while I was not watching. I wanted to stay in the loop. I just wanted the thing to remember.

So I went the other way: not more autonomy, more memory.

The approach: a project that remembers

I use Claude Cowork, where a "project" is basically a workspace Claude can read and write. So I gave that workspace a structure and a habit.

The structure is just folders of Markdown: a context area for who we are and what we sell, working areas for marketing and the website, and a decisions folder with a decisions log and a list of open questions.

The habit is the important part. Every meaningful task ends with a Memory Update: before calling it done, the assistant checks whether anything changed (a decision, an assumption, an open question, a risk) and writes it back to the right file. The next session reads those first.

That is it. No app, no database, no dependencies. But the effect compounds: after a few weeks the project actually knows the business, and it gets a little sharper each week instead of forgetting.

What it turned into

I cleaned it up, stripped every private detail, added a sanitized real workspace as a reference, and put it on GitHub as cowork-os, an open-source operating system for Claude Cowork. MIT.

It has three modules I actually use: a LinkedIn content system, an outcome-driven "missions" workflow, and a set of recurring automations.

The part worth calling out for builders: it started as "copy this folder, paste this installer." That works, but it is friction. So I also packaged it as an installable plugin. Now you add the marketplace and install it, and you get an always-on skill (the operating rules plus the memory protocol) and a handful of slash commands, with no copying.

If you build plugins, run claude plugin validate before you ship. It caught a YAML bug in one of my command files: a stray colon in the frontmatter that silently dropped all the metadata. Two minutes to fix, but it would have failed review.

The honest limitations

It is a convention, not magic. It only works if you keep the habit. The plugin today ships the runnable core (the rules plus the commands) but not the full templates, so the guided setup leans on the repo for the high-fidelity version. And it is tied to one tool, Claude Cowork, though the method itself is portable.

Try it, or tell me where it breaks

Repo: https://github.com/yempik-ai/cowork-os

I am genuinely curious how other people handle memory and context across AI sessions, and where this approach falls apart at scale. If you try it, tell me what felt clunky. I am iterating in public.