I've found value in architectural research before r&d tier projects like big changes to gfql, our oss gpu cypher implementation. It ends up multistage:

- deep research for papers, projects etc. I prefer ChatGPT Pro Deep Research here As it can quickly survey hundreds of sources for overall relevance

- deep dives into specific papers and projects, where an AI coding agent downloads relevant papers and projects for local analysis loops, performs technical breakdowns into essentially a markdown wiki, and then reduces over all of them into a findings report. Claude code is a bit nicer here because it supports parallel subagents well.

- iterative design phase where the agent iterates between the papers repos and our own project to refine suggestions and ideas

Fundamentally, this is both exciting, but also limiting: It's an example of 'Software Collapse' where we get to ensure best practices and good ideas from relevant communities, but the LLM is not doing the creativity here, just mashing up and helping pick.

Tools to automate the stuff seems nice. I'd expect it to be trained into the agents soon as it's not far from their existing capabilities already. Eg, 'iteratively optimize function foobar, prefer GPU literature for how.'