Has anyone found a efficient way to avoid repeating the initial codebase assessment when working with large projects?
There are several projects on GitHub that attempt to tackle context and memory limitations, but I haven’t found one that consistently works well in practice.
My current workaround is to maintain a set of Markdown files, each covering a specific subsystem or area of the application. Depending on the task, I provide only the relevant documents to Claude Code to limit the context scope. It works reasonably well, but it still feels like a manual and fragile solution.
I’m interested in more robust strategies for persistent project context or structured codebase understanding.
I moved back to Arch Linux after my 11 months old MBP died and took over a month to get it fixed. Not really looking at going back to macOS.
There are no aspect of the Apple OS that I miss and Linux desktop just works nowadays.
A set of Python scripts to automate the importing of financial transactions into Beancount. Been using it consistently for ths last 3 years to manage my finances.
There are several projects on GitHub that attempt to tackle context and memory limitations, but I haven’t found one that consistently works well in practice.
My current workaround is to maintain a set of Markdown files, each covering a specific subsystem or area of the application. Depending on the task, I provide only the relevant documents to Claude Code to limit the context scope. It works reasonably well, but it still feels like a manual and fragile solution. I’m interested in more robust strategies for persistent project context or structured codebase understanding.