#research workflow

Choosing Your AI Interface: A 2026 Guide to Browsing Agents and ChatGPT Atlas

With the 2026 arrival of ChatGPT Atlas, the web browser has moved from a static viewer to an active research agent. We compare task success rates, privacy models, and human-cleanup loads across the market leading tools.

In scientific and medical research, creating reusable evidence assets helps teams work more efficiently and maintain consistency across projects. A structured approach ensures that evidence can be accessed, reused, and applied across multiple workflows.

The process begins with centralizing storage, where all evidence is stored in a shared and accessible system. This is followed by the need to share across teams, ensuring that everyone works from the same trusted evidence base.

To support consistency, organizations must standardize evidence using structured formats and consistent tagging. This makes it easier to organize and retrieve information. Finally, teams should enable reuse by applying validated insights across different projects instead of repeating the same analysis.

I did end up using my entire workday to clean up my research graph ^__^U

on the left, how the graph looked after I cleaned the first 100-ish pages (it didn't occur to me to make a screenshot right at the beginning). on the right, how the graph looks now, with "just" 504 pages. the graph isn't showing all of them however, since I have hidden several of them for the purpose of making the graph clearer.

in total I must have deleted between 250-300 pages. I have also made new pages, so in the end it's still just above 500.

the "slush pile" is one of the new pages. now whenever I import a paper with zotero, it gets automatically tagged as "slush pile", and I'm meant to periodically check the papers on that tag and either tag it with a project (if I think it'll be useful) or just delete it entirely.