Today, I deleted UnivMind and OpenCairn.
I took down the servers and removed the repositories from GitHub. I could have left them there. But I did not want to keep projects online when they were difficult to explain, even to myself, or when their behavior felt uncertain.
I also kept thinking about how those projects might look to someone visiting my GitHub. They could easily become the standard by which people judged what I had built.
I did not like that.
UnivMind
UnivMind was an AI learning service for university students.
I assumed that university course materials would be scattered across PDFs, slide decks, announcements, and documents. They were. During exam season, finding the material, organizing it, and understanding it again becomes work in its own right.
I wanted students to upload their materials, organize them with AI, ask questions, and study in one place.
UnivMind worked.
It was not a completely broken project. The MVP ran, and real users tried it. But over time, it started to feel thin.
The problem was not that it lacked features. The problem was whether there was a reason to keep pushing it.
The market already had similar products: NotebookLM, ChatGPT, Claude, Gemini, and a growing number of AI study tools for university students.
Students already had alternatives. They were also more careful than I expected about paying for another service like this.
At first, I thought I could win by simply making it better. Then different questions started to bother me.
Could I really win in this market? Why would a student choose my service specifically? Did the product have a moat? Was I holding on only because I knew how to build it?
In the end, I decided that UnivMind worked, but it was not sharp enough.
So I deleted it.
OpenCairn
OpenCairn grew out of a pivot from UnivMind.
It began as an AI learning service for university students, but the phrase “AI note-taking app” soon felt too small. I did not want to build just another tool that answered questions about PDFs.
I wanted a system that could hold documents, sources, knowledge graphs, AI workflows, evidence, and an agent ledger in one place. I wanted a knowledge workspace that preserved the reasoning and evidence behind work done with AI, instead of producing only one-off chats.
That was why I chose the name OpenCairn. I was imagining something like an open-source AI Knowledge OS.
The vision was big.
The implementation did not keep up.
The agent I considered central never worked reliably enough. I built it, but I could not turn it into the stable workflow I had imagined.
The problem definition grew too large, the implementation grew too complicated, and the gap between what I wanted to explain and what actually worked kept widening.
OpenCairn was a different kind of failure from UnivMind.
UnivMind worked but felt thin. OpenCairn had a large vision but did not work well enough.
One was a product I could build without a strong reason for people to use. The other had a large direction, but the implementation could not follow it.
Building Has Become Easier
AI development tools have dramatically lowered the barrier to building an MVP.
Services that would once have been difficult to build alone can now be put together surprisingly quickly with tools such as Claude and Cursor. Since high school, I have built and operated an electronic attendance system, school services, and AI chatbots in much the same way. I have also recruited real users and watched them use what I made.
At first, that felt like a superpower.
I can build this by myself. If I have an idea, I can turn it into a service.
UnivMind and OpenCairn changed that feeling a little.
Building became easier.
Creating a reason for people to use something did not become easier at all.
The ability to build an MVP is different from the ability to build a good product. The ability to build a good product is different from the ability to build a good business.
Being able to write code with AI does not make a customer problem stronger. A working service does not automatically make people pay. A big vision does not automatically make a good product.
AI has also made it possible for more people to build similar products faster.
That is why I think choosing the problem matters more than implementing another feature.
What problem are we solving? Is it strong enough? Who pays? Why now? Why me? What is the moat? How deeply does the product enter a customer's workflow or tacit knowledge?
Those questions matter more now.
Deleted, But Not Gone
I deleted UnivMind and OpenCairn.
But I do not think they disappeared completely.
UnivMind showed me the limits of building an AI learning service for university students. OpenCairn taught me that a large vision does not become a product by itself.
Neither project was finished. Both were experiments that changed how I judge what to build.
I used to ask whether I could build something. Now I want to ask why I should build it.
I used to think adding features was how a product came together. Now I pay more attention to the strength of the problem and the user's context.
I used to find the ability to build something with AI exciting in itself. Now that AI makes building easier, I think choosing the problem carefully matters even more.
Deleting these projects was not simply giving up on them. It was deciding what I would no longer hold on to.
I still do not have a complete answer about what comes next. But one thing is clear.
Next time, I want to build something smaller. I want to start with a more concrete problem.
I want to look for problems that actually reduce someone's money, time, or repetitive work, rather than visions that merely look impressive.
I want to build a product people have a real reason to use, rather than a polished AI product that is only interesting to look at.
I deleted UnivMind and OpenCairn, but I want to keep what I learned from them.