A few years ago, I wrote about how the library’s card catalog is the right metaphor for organizing our digital information. The physical catalog works because it connects all our information exactly how we need for our personal knowledge bases.

But I missed something fundamental: the card catalog in a real Library doesn’t maintain itself.

Behind every well-organized library is a team of people constantly working to keep information accessible. They’re cataloging new acquisitions, updating subject headings, cross-referencing related materials, culling outdated items, all before you ask. When you walk into a library and find exactly what you’re looking for, it’s because of the wonderful staff there.

The reason we struggle with digital organization isn’t that we lack filing systems: there are too many systems out there to count. It’s that we’re trying to be both the user and the entire staff, and we simply don’t have the time.

What Library Staff Actually Do

Cataloging: Every new item gets metadata assigned: and not just title and author, but subject headings, classifications, and keywords.

Cross-referencing: Related materials get linked. If you’re checkout out the Cold War, the librarian has already connected biographies, primary source documents, and relevant periodicals.

Collection maintenance: Old, outdated, or rarely-used items get evaluated. The collection stays relevant through active curation, not just accumulation.

Discovery support: Librarians create displays, reading lists, and subject guides that surface relevant materials you didn’t know to ask for.

Answer questions: When search isn’t cutting it, a human who understands the collection and knows where things are can help.

All of this is proactive. The organization work happens continuously in the background, an act of renewal.

We’ve Been Trying to Do This Ourselves

With all of our notes, documents, bookmarks, photos, and emails, we are attempting to be our own librarians. We make folders. We add tags when we remember. We try to search when we need something. We promise ourselves we’ll organize everything soon.

It doesn’t work because the ratio is impossible: one person cannot be both a library’s sole user and its entire staff. The cataloging work scales with the collection size and our available time does not.

Every productivity system eventually fails for this reason. We build the perfect folder hierarchy, create the ultimate tagging system, install the best knowledge management software; then life happens. Files pile up. Tags become inconsistent. The system that was supposed to save us time becomes another thing we feel guilty about not maintaining.

The problem isn’t our discipline – the problem is structural.

A Personal Library Staff?

For the first time, we can actually build something resembling “a proactive staff.”

Cloud AI agents can do some of this work, but at the expense of sending all our private information up to their servers constantly…

So recently I’ve been learning about and working with local models, the ones you can run on your phone or laptop in a truly private, offline way. I’ve had success doing the systematic, repetitive organization work that can improve our personal knowledge systems and finally make them workable over time.

Here’s what I’ve learned is possible: I can build agents that process new documents as they arrive, extracting metadata and assigning categories based on my existing system. When patterns emerge, they suggest new categories I hadn’t thought of. I’ve programmed experiments to scan my collection for connections – linking meeting notes to project documents, research papers to relevant bookmarks, photos to calendar events.

They can handle maintenance too. Flagging duplicates, identifying outdated information, highlighting orphaned items that have no connections to anything else. As documents move into my archive, agents generate abstracts so I don’t lose track of what I once knew. They create reading lists organized by theme, produce regular digests of what I’ve saved recently.

And they can “learn” from how I search. Every query I run is information about what matters to me and how I think about my personal context. An agent can use that to preemptively improve discoverability of materials I’ll likely need, without me having to stop and manually organize everything first.

None of this requires me to stop what I’m doing to organize. The agents can work in the background.

Private and local is the future

Your personal context contains your most sensitive information: work documents, personal notes, financial records, health information, etc. Sending this to a cloud AI service doesn’t feel right. And we don’t have to.

A Mac Mini can run models that will handle this work. No subscriptions. No data leaving your network. Your local private “staff” works for you, not for the cloud provider that trained them.

Specialized agents, not one super-agent

Local models are small, so it is prudent to build many small agents instead of one big one.

• A filing agent that runs daily to categorize new items
• A connection agent that finds relationships between materials
• A maintenance agent that flags issues and duplicates
• A summary agent that creates abstracts and digests
• A search agent that learns from your queries to improve indexing
• Etc

Each agent should have focused instructions tailored to your specific organization system. You’re not prompting ChatGPT to “organize my files.” You’re running custom, tailored local programs that know your taxonomy, your terminology, and your priorities.

Every time you search for something or ask a question, you’re providing data about what matters to you and how you think about your information. Every time you manually connect two documents, you’re teaching the system about relationships it should look for. Every time you can’t find something, you’re revealing a gap in the organization.

These patterns should become instructions for proactive work. If you frequently search for “client feedback from Q3,” your agents should learn to automatically tag and group client feedback by quarter. If you often link legal documents to project files, your agents should start suggesting those connections preemptively.

Local Agents Change the Game

The library card catalog works because it is maintained by people whose full-time job is keeping information accessible. Personal knowledge management has failed for most of us because we’re trying to do that work ourselves while also being the library’s primary user.

Local, private agents can solve this by becoming your library staff. They can do the proactive, systematic organization work in the background. They can learn from your usage patterns. They can run continuously without requiring your attention. And because they run locally, your information stays yours.

You don’t need better organizational discipline. You need staff. And for the first time, you might can actually have them.

I’m building toward this vision in a product called new.space. Run local models on your iPhone today. Give it a try.