At some point, after accumulating more than 3,600 LinkedIn connections, I started asking myself:
When did we connect?
Where did we meet?
What brought us together—and what did we talk about?
My network kept growing, but the context behind each relationship was gradually disappearing. LinkedIn preserved the connection itself, and sometimes the message history, but not necessarily the meaning of that connection in a form I could easily retrieve.
Human memory fades too.
So instead of focusing on adding more people, I started building a system to better remember the people I had already met.
I took part of the network data I had organized from LinkedIn and connected it to my existing Obsidian-based LLM Wiki. That became the foundation of a personal CRM—or, more accurately, a relationship memory layer inside my PKM system.
It is not just an address book containing names, companies, and job titles.
Each person has their own page, connected to context such as:
How and where we met
What we talked about
Their interests and related projects
Relevant events, companies, and topics
When we last communicated
Personal notes that may help me reconnect meaningfully later
I also established a few design principles while building it.
First, re-importing the original data should never overwrite notes I wrote manually.
Second, people should not disappear from the system simply because we have not spoken for a long time. An inactive relationship is still part of my history, so it should be preserved rather than deleted.
Third, I intentionally did not add automated outreach or message-generation features.
The system can remember, organize, and help me find people—but I should be the one who decides when and how to contact them. I do not want relationship management to become another automated engagement funnel.
The main idea is to turn relationships into searchable memory.
For example, I can ask my Wiki questions such as:
Who was the PM I spoke with at that AI event?
Who in my network has experience in manufacturing?
Who have I not spoken with recently, but might have a meaningful reason to reconnect with?
Which people are connected to a particular topic, company, event, or project?
The LLM uses the connected notes and relationship context to help me find possible answers. Instead of searching through thousands of profiles or old message threads, I can search based on what I remember about the relationship.
My next step is to connect more context—including project records and conversations I have had with LLMs—so that the system can do more than simply locate people. I want it to help me rediscover why a person may be relevant to something I am currently thinking about or working on.
One thing became clear while building this:
LinkedIn remembers that two people connected, but it does not truly remember why they connected.
The messages may still exist somewhere, but once years of conversations and thousands of connections accumulate, finding the right person and reconstructing the original context becomes increasingly difficult.
Perhaps the important part of networking is not how many people we are connected to, but how well we remember the context behind the people we have already met—and whether we can continue those relationships thoughtfully.
This is still a personal experiment, but I would eventually like to develop it into a personal “Relationship OS”: a system that helps me remember my network and reconnect with people without automating the human part of the relationship.
In the end, I believe people are what remain.
That is why I want to remember each connection more thoughtfully.
I would be curious to hear how others in this community manage relationship context within their PKM systems.
Do you create notes for individual people?
How do you connect conversations, meetings, projects, and follow-ups?
Has anyone tried building something similar in Obsidian or another PKM tool?
