https://github.com/lutzleonhardt/casefile
During my work at Brokk last year we developed a task-slicing system. The idea was to have a bunch of tasks where each task can be implemented and verified in isolation. So I can mentally focus only on one task at a time with checking the evidence it works as expected. Most follow-up tasks are based on their predecessor(s).After my time at Brokk I picked up this idea and developed Casefile. To...
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