llm VS dioxus

We're open-sourcing a project that lets you build simple automations/agent workflows that use LLMs for different tasks. Kinda like Zapier or IFTTT but focused on using natural language to accomplish your tasks.It's super early but we'd love to start getting feedback to steer it in the right direction. It currently supports OpenAI and local models through llm.

> Tensorflow is a C++ framework that has Python bindings and a Python library, but when the models are served they are running on C++

Sure, and it's only a simple 20 step process that involves building Tensorflow from source. Yeay!

https://medium.com/@hamedmp/exporting-trained-tensorflow-mod...

Let me see what the process for compiling a LLM written in Rust is....

https://github.com/rustformers/llm

    cargo install llm-cli

Floneum is a graph editor for local AI workflows. It uses llm to run large language models locally, egui, and dioxus for the frontend, and wasmtime for the plugin system. If you are interested in the project, consider joining the discord, or building a plugin for Floneum in rust using WASI

Run inference for Large Language Models on CPU, with Rust (https://github.com/rustformers/llm)

On the rustformers github page I see that one of the commands to generate the answer is llm llama infer -m ggml-gpt4all-j-v1.3-groovy.bin -p "Rust is a cool programming language because", my basic idea for now is to change the Tauri app to let it do -p prompt, which receives from my code through the link or through a shared variable (if I don't use the link and start different times your app)

wonnx has done some fantastic work in this regard, so that's where we plan to start once we get there. In terms of general discussion of alternate backends, see this issue.

We have a web components example here: https://github.com/DioxusLabs/dioxus/blob/fd21c971038840130f...

Everything should work like normal except: attributes are not typed, custom event listeners must be implemented with web-sys

I think something like https://dioxuslabs.com could deliver native, cross platform apps and win back mobile.

While we took an in-depth look at Dioxus in this tutorial, there is still so much to learn. Luckily, Dioxus provides detailed documentation with references and cookbooks to guide developers. Make sure you check it out, and feel free to comment below with any questions.

You can probably imagine the challenges of integrating such a system in a robust way that does a good job and improves on the status quo. We felt like the Tauri implementation worked well for Tauri apps. But there’s a problem: it was so tightly coupled to Tauri that the work couldn’t be enjoyed by other projects (not even the ones like Dioxus who were using Tauri’s underlying technology of Tao + Wry).

Floneum is a graph editor for local AI workflows. It uses llm to run large language models locally, egui, and dioxus for the frontend, and wasmtime for the plugin system. If you are interested in the project, consider joining the discord, or building a plugin for Floneum in rust using WASI