Recall.ai vs llama.cpp — Comparison

Recall.ai

Recall.ai provides an API to get recordings, transcripts and metadata from video conferencing platforms like Zoom, Google Meet Microsoft Teams, and mo

Record video conferences through a bot joining the call. Best when explicit recording consent is needed, or for building AI agents. Record video conferences and in-person meetings through a desktop app, without a bot in the call. Best for a stealthier recording experience. “Legal tech is high stakes. Working with Recall.ai, we had scalable Zoom meeting support ready in under two months, rather than six." “Recall.ai allows us to build meeting recording features without worrying about infrastructure. It has helped us move faster than we could have with an in-house build." "We’re building an AI Scribe for our doctors, and Recall.ai was the first piece of infrastructure I pushed to bring in. I’d seen how seamlessly it handled meeting data at my last company, which made choosing it again an easy call." "Recall.ai's Meeting Bot API saved us from months of pain. One integration, extremely reliable, and we launched our meeting bot feature in days." “Once we started using Recall.ai's Desktop Recording SDK to power Mem’s meeting recording experience, the painful edge cases that we had to chase on the support side went to zero." “Recall.ai allows us to operate reliable, enterprise-scale meeting transcriptions without worrying about infrastructure or security."

llama.cpp

LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.

Getting started with llama.cpp is straightforward. Here are several ways to install it on your machine: Once installed, you'll need a model to work with. Head to the Obtaining and quantizing models section to learn more. The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud. Typically finetunes of the base models below are supported as well. Instructions for adding support for new models: HOWTO-add-model.md After downloading a model, use the CLI tools to run it locally - see below. The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with llama.cpp: To learn more about model quantization, read this documentation For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/ If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT: The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS, and macOS. It can be used in Swift projects without the need to compile the library from source. For example: The above example is using an intermediate build b5046 of the library. This can be modified to use a different version by changing the URL and checksum. Command-line completion is available for some environments. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page.

Recall.ai

llama.cpp

Recall.ai

llama.cpp

Recall.ai

llama.cpp