cavewoman
Caveman prompting, measured. A two-channel evaluation protocol scoring what input and output compression actually cost LLMs in dollars, accuracy, and surface-text fidelity across seven models and five benchmarks.
Take a look →OPEN SOURCE, IN FOCUS
embeddings-benchmark/embedders-dilemma
Code, datasets, and results for "The Embedder's Dilemma: LLMs Are Better, but at What Cost?" (COLM 2026). MTEB(LLM) benchmarks 10 LLMs and 26 embedding models on 37 tasks with full cost and throughput accounting.
Saved projects stay in this browser. No account needed.
TAKE A CLOSER LOOK
The repository is the place to check installation steps, supported platforms, the license, and current maintenance before choosing a tool.
Read the documentation on GitHub ↗Find projects in the same areas, or search the archive for a different approach.