Show HN: Yappy – A Python TUI to automate LinkedIn yapping(github.com)
github.com
Show HN: Yappy – A Python TUI to automate LinkedIn yapping
https://github.com/JienWeng/yappy
2 コメント
Interesting idea. The part that caught my attention is the automation of commenting. LinkedIn has been getting more and more “performative” lately, so I can see why someone would experiment with tools like this.
I’m curious though how are you handling context when generating comments from the feed? Is it summarizing the post first or just using the raw text?
I’m curious though how are you handling context when generating comments from the feed? Is it summarizing the post first or just using the raw text?
Doesn't this just add to the problem? I do find it funny though, having an AI automate this.
Absolutely despise the whole LinkedIn nonsense posts though. That said, I don't really browse it leisurely lol.
Absolutely despise the whole LinkedIn nonsense posts though. That said, I don't really browse it leisurely lol.
I got tired of the performative culture on LinkedIn. The platform mostly feels like people just yapping at each other all day to farm engagement, so I decided to build a CLI tool to do the yapping for me. It's called Yappy (because we just yap yap yap).
It is an open-source Python TUI that automates your LinkedIn engagement directly from the command line. It logs in, reads your feed, and uses the Gemini API to generate context-aware comments and drop likes based on your prompt parameters. Everything runs completely in the terminal, so you never actually have to look at the feed yourself.
I know AI-generated comments and web scraping are controversial, and LinkedIn's TOS strictly prohibits this kind of automation. This is built purely as an experimental toy project and a proof-of-concept for integrating LLMs with terminal interfaces. If you actually decide to run it, definitely use a burner account, as LinkedIn will likely restrict you if you run it too aggressively.
I am mostly looking for technical feedback on the Python code architecture and the TUI implementation.
Would love to hear your thoughts. Roast my code or drop a PR if you find the concept funny.