These are some tools I'm using for lower usage and better accuracy:

• rtk-ai : Filters and compresses command-outputs AI have to go through. https://github.com/rtk-ai/rtk
• codegraph : Traces function-to-function calls in codebase, reduces exploration. https://github.com/colbymchenry/codegraph
• RAG : Retrieval Augmented Generation, used in documentation for chunk-based text-recognition, reduces exploration. https://www.ibm.com/think/topics/retrieval-augmented-generation
• headroom - Haven't tried yet : Middleware compression-layer, compacts prompts before arriving at LLM. https://github.com/headroomlabs-ai/headroom
• ggrun - Haven't tried yet: llama.cpp model optimization. https://github.com/raketenkater/ggrun

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Quantprobe - llama.cpp model quant optimization. https://github.com/FedericoTs/quantprobe

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