52 karma · joined March 2, 2021
To be fair, I saw that you give credit, but it's kind of disappointing still given that prob 98% of the project is the work of the Rubberduck contributors. I know Rubberduck is not that active any more, but it's still disappointing.
I've created "StoryTeller", a multi-modal app that quickly generates audio stories for pre-school kids.
StoryTeller is built with the following libraries:
- Fastify - Next.js - shadcn/ui - ModelFusion - Zod
The following AI APIs are used:
- OpenAI (story generation, embeddings) - Eleven Labs (tts) - Lmnt (tts) - thanks for sponsoring the project with credits! - Stability (images)
StoryTeller is an exploratory web application that creates short audio stories for pre-school kids.
It used speech-to-text, llms, text-to-speech, embeddings and image generation.
StoryTeller is built with the following libraries:
ModelFusion Fastify Next.js shadcn/ui Zod
The following AI APIs are used:
OpenAI Eleven Labs Lmnt Stability
https://www.anyscale.com/blog/fine-tuning-llama-2-a-comprehe...
I expect most models to become multi modal in the future and am building towards. A lot of the core logic of agents will nevertheless be text based imo, so that’s a central piece, but I already added text to image and speech to text, and plan to add text to speech next.
https://github.com/lgrammel/modelfusion
It is only getting limited traction so I’m wondering if I’m missing something fundamental with the approach that I’m taking.
https://github.com/lgrammel/modelfusion
It lets you stay in full control over the prompts and control flow while make a lot of things easier and more convenient.
- Type inference and validation: ModelFusion uses TypeScript and Zod to infer types wherever possible and to validate model responses.
- Flexibility and control: AI application development can be complex and unique to each project. With ModelFusion, you have complete control over the prompts and model settings, and you can access the raw responses from the models quickly to build what you need.
- No chains and predefined prompts: Use the concepts provided by JavaScript (variables, functions, etc.) and explicit prompts to build applications you can easily understand and control. Not black magic.
- More than LLMs: ModelFusion supports other models, e.g., text-to-image and voice-to-text, to help you build rich AI applications that go beyond just text.
- Integrated support features: Essential features like logging, retries, throttling, tracing, and error handling are built-in, helping you focus more on building your application.
Thanks for the feedback, good points!
I plan to release a v1.0 in the coming days and I'm looking for feedback on what to improve.
As part of a larger project that I'm working on, I've created a playground where you can explore some newer (and older) parts of JS, e.g. the ?? and ?. operators, and see how existing code could be changed to use them.
Would love to hear feedback, in particular since this idea is pretty early :)