I recently open sourced a desktop AI assistant I originally built for myself.
The motivation was simple. I wanted to try a few popular AI desktop tools, but most of them were paid and didn’t quite fit how I wanted to use them. I looked for open source alternatives and found a few, but none felt complete enough for real day to day use.
So I decided to build one.
Over the next few days, I put together a desktop assistant focused on handling deeper conversations like multi step reasoning, follow up questions, and interview style discussions rather than just surface level chat.
What surprised me was the response after open sourcing it. The project started seeing steady adoption, with people cloning the repo, reading through the code, and experimenting locally. Stars came later, often after people had already tried it.
A few things I learned from this process:
Open source traction is delayed. People often try things before publicly endorsing them.
Forks and clones were a much stronger early signal than stars.
Desktop tools behave very differently from web apps in how people discover and adopt them.
Clear motivation matters more than a long feature list.
I am still figuring out what the long term direction of the project should be, but the last week has been a useful learning experience about how developers actually evaluate and adopt tools.
Happy to answer questions or hear similar experiences from others.
Nive11•1h ago
The motivation was simple. I wanted to try a few popular AI desktop tools, but most of them were paid and didn’t quite fit how I wanted to use them. I looked for open source alternatives and found a few, but none felt complete enough for real day to day use.
So I decided to build one.
Over the next few days, I put together a desktop assistant focused on handling deeper conversations like multi step reasoning, follow up questions, and interview style discussions rather than just surface level chat.
What surprised me was the response after open sourcing it. The project started seeing steady adoption, with people cloning the repo, reading through the code, and experimenting locally. Stars came later, often after people had already tried it.
A few things I learned from this process:
Open source traction is delayed. People often try things before publicly endorsing them. Forks and clones were a much stronger early signal than stars. Desktop tools behave very differently from web apps in how people discover and adopt them. Clear motivation matters more than a long feature list.
I am still figuring out what the long term direction of the project should be, but the last week has been a useful learning experience about how developers actually evaluate and adopt tools.
Happy to answer questions or hear similar experiences from others.