Hi, yes there are some blog posts:

https://brokk.ai/blog/brokk-under-the-hood

That officially makes you an early adopter, thanks!

Yes, it's basically just parsing for declarations. (If you doubleclick on any context in the Workspace it will show you exactly what's inside.)

You have to import the dependencies via File -> Decompile Dependency and then it gets parsed like the rest of your source, only read-only.

I have a love-hate relationship with Java, mostly love lately, the OpenJDK team is doing a great job driving the language forward. It's so much faster than Python, it's nice being able to extend a language in itself and get native performance.

Since we're just using Jlama to debounce the LLM requests, we can use a tiny model that runs fine on CPU alone. The latest Jlama supports GPU as well but we're not using that.

We implemented a multi-step process to find the required context:

1. Quick Context Shows the most relevant files based on a pagerank algorithm (static analysis) and semantic embeddings (JLama inference engine). The input are the instructions and the AI workspace fragments (i.e. files).

2. Deep Scan A richer LLM receives the summaries of the AI workspace files (+instructions) and returns a recommendation of files and tests. It also recommends the type of inclusion (editable, read-only, summary/skeleton).

3. Agentic Search The AI has access to a set of tools for finding the required files. But the tools are not limited to grep/rg. Instead you can: - find symbols (classes, methods, ...) in the project - ask for summaries/skeletons of files - provide class or method implementations - find usages of symbols (where is x used?) - call sites (in/out) ...

You can read more about this in the Brokk.ai blog: https://brokk.ai/blog/brokk-under-the-hood

no, but I've tested on intellij (~5M loc, takes forever to import b/c of delombok, do not recommend)

I tested it with Ghidra recently and got very good results

I am a huge supporter of completely re-working the IDE UI as well. I'm not arguing for keeping the existing IDE interfaces. I like that folks are experimenting with entirely new interfaces. In fact, I'd go further and suggest that all of the overly complex interfaces used on any sort of content-creation app, like Unity, Unreal, Photoshop, as well as code IDEs, will eventually be completely refactored to remove all the old complexity in favor of either chat-based or other AI-driven interfaces. My point is simply that there are too many new AI-driven IDEs for folks to try out, even the developers of those IDEs. Many of the features in Brokk that were seemingly described in the Brokk 101 blog video as "differentiators" are existing Copilot features. Has the author ever used Copilot? Or just Cursor? Or another AI variant?

I'd love to see things like Brokk experiment a bit more with what other information to include in our git repositories, besides the code, that helps improve AI-based code generation. For example, perhaps the repo should include more design information about the look-and-feel, as visual information or Figma files, rather than just, say the CSS and HTML. Or it might help if the repository included more business requirements so that the AI has better information to guide prioritization of changes. Obviously other bits, like coding standards, should be included as well, though perhaps using a larger context might mitigate the need for coding standards if the generated code followed the existing code (which often doesn't happen).

I think that's what's going to happen over time. We're going to be writing more and more code, but supervising an AI mostly.

The big problem is that we're treating the AI as an all knowing oracle. And probably what we should be doing is treating the AI as a colleague -- allowing it to ask questions about the code base to develop the subtle clues.

Often what happens is that subtlety is lost on the code base, and sometimes, AI will think it's an outright error, when in fact it's completely on purpose.

Comments go a long way towards this end, but in large legacy codebases, comments may not exist, and the coders expected people to understand at first glance the code was correct.

Test driving Junie, I've had it remove a feature it thought was broken code, and then it fixes the unit tests, instead of trying to understand if the unit test was actually broken or the feature was broken.