I kept drowning in ML papers while learning, so I built an app that auto-connects each new one to what I've already saved

Being deep in the ML firehose — new papers daily, half of them building on something I'd read weeks earlier — means the hard part is never finding papers. It is remembering how they connect, and knowing what is even worth reading next.

So I built Trelk to fix my own version of this. You save a paper, article, PDF, or note, and it summarizes, tags, and embeds it on-device, then automatically surfaces the connections to things you've already saved — same-topic links and cross-topic bridges you'd have missed. There's semantic + keyword search, a knowledge graph, chat over your own library that answers with citations to your sources, and spaced-repetition review so what you save actually sticks.

The part that's most relevant for people starting out: there are community-curated content lists — browsable, open reading collections that anyone can submit to. So instead of staring at a blank library wondering where to begin, you can pull in a curated path someone else built on a topic, then watch it connect to whatever you already have. It turns "what should I even read?" into a starting point rather than a wall.

And it all runs on-device (small Qwen model for summaries, Gemma embeddings), no account, fully offline, data in plain SQLite + Markdown you can export anytime. Nothing locked in anyone's cloud.

30-second demo of a new paper finding its neighbors: https://www.youtube.com/shorts/QQ4_yxka5-w

More detail: https://trelk.app

I'm the creator and I think a lot of us live this pain. If you're early in ML: would browsable community reading lists that auto-integrate with your own library actually help you get oriented, or is the bottleneck somewhere else for you? Genuinely want the feedback.

iOS now, Android coming really soon.