Literal AI (@literalai) 's Twitter Profile
Literal AI

@literalai

Literal AI is a collaborative observability, evaluation and analytics platform for building production-grade LLM apps.

You might know us from @chainlit_io!

ID: 1804130211530768385

linkhttps://literalai.com calendar_today21-06-2024 12:32:33

8 Tweet

100 Followers

3 Following

Chainlit (@chainlit_io) 's Twitter Profile Photo

Announcing Chainlit πŸ”—πŸ’‘- Build & share Python LLM apps in minutes⚑️ Chainlit is an open-source 🐍 Python package that lets you create ChatGPT-like UIs on top of your existing code effortlessly! `pip install chainlit` github.com/Chainlit/chain… πŸ§΅πŸ‘‡

Dan Constantini (@danoandco) 's Twitter Profile Photo

Great example by Mark Edmondson on how GenAI can improve learning with personalized lessons tailored to your abilities and experiences. Delivered via an interactive user interface using Chainlit LangChain Anthropic πŸ”₯

willy douhard (@willy_douhard) 's Twitter Profile Photo

Chainlit is built for multi modality but was not supporting voice use cases … until today! Chainlit developers can now access user audio streams and build voice-based LLM applications πŸ”₯ 🧡

LlamaIndex πŸ¦™ (@llama_index) 's Twitter Profile Photo

Build a Blazing-Fast RAG Chatbot with Llama3 on Groq Inc, Chainlit, and LlamaIndex πŸ¦™ ⚑️ This is a neat resource by Jayita B. on teaching you how to not only build an advanced RAG indexing/query pipeline, but also turn it into a full-stack application with rapid response

Build a Blazing-Fast RAG Chatbot with Llama3 on <a href="/GroqInc/">Groq Inc</a>, <a href="/chainlit_io/">Chainlit</a>, and <a href="/llama_index/">LlamaIndex πŸ¦™</a> ⚑️

This is a neat resource by Jayita B. on teaching you how to not only build an advanced RAG indexing/query pipeline, but also turn it into a full-stack application with rapid response
Dan Constantini (@danoandco) 's Twitter Profile Photo

Literal AI is now in public beta πŸŽ‰ Literal AI is the multimodal LLM app observability and evaluation solution for developers and product owners. Supports uses cases from simple LLM calls to complex agentic systems across various modalities: text, image and audio. 🧡

Dan Constantini (@danoandco) 's Twitter Profile Photo

πŸŽ‰ Introducing Literal AI Prompt and LLM A/B Testing πŸ“ˆ Gradually roll out new prompts or LLMs in production and compare performance metrics, reducing risk πŸš€ Product teams can independently deploy prompt or LLM updates, speeding up iteration and freeing engineering resources

Dan Constantini (@danoandco) 's Twitter Profile Photo

Build your own Reflection Playground with Chainlit, serving Matt Shumer new Reflection-70b model! πŸ€— Hugging Face for the weights πŸ”§ Baseten for inference πŸ–₯️ NVIDIA TensorRT 🌐 Chainlit for app deployment

Chainlit (@chainlit_io) 's Twitter Profile Photo

πŸš€ New LiteLLM (YC W23) Integration πŸš€ Track & log all your LiteLLM calls with Literal AI in just 2 lines of code! LiteLLM allows you to interact with 100+ LLMs (OpenAI, Anthropic, Mistral AI, etc.) seamlessly using a consistent OpenAI-compatible format, either use their python