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cavewoman

danielle34/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.

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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.

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borhen68

tokentamer

A drop-in proxy that compresses bloated code context in real-time, cutting LLM API costs by 50–80% without losing what the model actually needs to know.

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cloudrift-ai

emmy

Benchmark and deploy optimized LLM models on GPU servers with vLLM or SGLang. Chose from a list of optimized recipes for popular models or create your own with custom configurations. Run benchmarks across different GPU types and configurations, track results, and share experiments with the community.

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