When I was working on a couple of open-source projects and publishing them to different package managers like PyPI, crates.io, and Homebrew, I was curious to see if anyone was downloading them and what the adoption looked like. It was a pain to go to each package manager and look for statistics.
That's when I decided to build a simple TUI dashboard for package intelligence. I wanted it to go beyond basic package details and gradually become more of an engineer's tool for understanding open-source packages.
While developing it, I started seeing a broader use case like integrating it with coding agents through MCP and providing this metadata about open-source packages, so agents can make package suggestions based on actual data points rather than just what they already know.
I'm still exploring where this could go and would be interested to hear what signals you look at when evaluating an open-source package.
When I was working on a couple of open-source projects and publishing them to different package managers like PyPI, crates.io, and Homebrew, I was curious to see if anyone was downloading them and what the adoption looked like. It was a pain to go to each package manager and look for statistics.
That's when I decided to build a simple TUI dashboard for package intelligence. I wanted it to go beyond basic package details and gradually become more of an engineer's tool for understanding open-source packages.
While developing it, I started seeing a broader use case like integrating it with coding agents through MCP and providing this metadata about open-source packages, so agents can make package suggestions based on actual data points rather than just what they already know.
I'm still exploring where this could go and would be interested to hear what signals you look at when evaluating an open-source package.