For "vibe research" across multiple sources, I've been using an AI setup that monitors topics I care about and summarizes only what's relevant.

Helps reduce the information overload while still catching context quickly. Instead of browsing 10 newsletters and feeds manually, I get a digest of what actually matters to my current interests.

Not quite the same as deep literature review, but effective for staying on top of a field without drowning in it.

Not for anything seriously published, but - yeah I use ChatGPT constantly to evaluate essay ideas, critique arguments, and give me more reading material ideas for whatever topic I'm thinking about.

I think this is truly the best use-case of LLMs, actually. It functions as a kind of hyper-informed assistant.

I tried it last year, and compiled a list of projects in one category herehttps://openreview-copilot.eamag.me/projects and 2025 retro here https://eamag.me/2025/links/Links-And-A-Retrospective-Of-202...

The biggest issue is to be able to identify good ideas, ones that are useful, novel and doable. "Research taste" is the thing people develop over years!

I don't think we're there yet, and "vibe" research producing slop now, but creating tools for other researches to move faster sounds way more promising

I'm not a researcher but i did 'write' a 'paper' for personal consumption to dissect a thought experiment I had on thriving in the Age of AI, Neural Networks, and Quantum Logic.

It examined the psychological and strategic archetypes that determine success or failure during periods of radical technological disruption, using the internet revolution (1995-2015) as a historical baseline. I don't know if it was any good, but it was a fun few hours of exploration.