Show HN: Keep Your Next Viral AI App Free for Longer with Local Embeddings
Function LLM is a new tool that allows developers to generate local embeddings, potentially saving up to 60% on OpenAI costs while enhancing privacy and requiring minimal implementation effort.
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Developers can significantly reduce their costs associated with OpenAI by utilizing local embeddings through a new tool called Function LLM. This lightweight library allows for the generation of text embeddings directly on users' devices, which can save up to 60% on monthly expenses. Traditional methods rely on OpenAI's per-token pricing, which can accumulate substantial costs when serving a large user base. Function LLM integrates with Nomic's advanced embedding model, offering better performance than OpenAI's existing models. The library is designed to be simple, requiring only a single line of code to implement. It compiles Python functions for various platforms, enabling on-device processing without the need for cloud-based services. This approach not only reduces costs but also enhances user privacy by keeping data local. Function LLM is positioned as a solution for developers looking to optimize their AI applications while avoiding the pitfalls of cloud dependency. The developers encourage users to try the demo and offer credits for early adopters.
- Function LLM can save developers up to 60% on OpenAI costs by generating embeddings locally.
- The library supports Nomic's embedding model, which outperforms OpenAI's text-embedding-3-small model.
- It requires only one line of code to implement, making it user-friendly for developers.
- The tool enhances user privacy by processing data on-device rather than in the cloud.
- Early adopters can receive credits for signing up and trying the service.
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