Master Serverless LLM Apps with Amazon Bedrock

📖 TUTORIALS & CASE STUDIES

Build an AI-powered LinkedIn profile reviewer: OpenAI Assistants API and Gpt-4-vision

read time: 10 minutes
This article provides a deep dive into building an AI-powered LinkedIn profile reviewer using OpenAI's Assistants API and Gpt-4-vision. The author shares their experience in creating a prototype that analyzes LinkedIn profiles, provides feedback, and even scores the profile. The article includes code snippets and explanations of the workflow, making it a valuable resource for developers interested in leveraging AI for profile optimization. Read more

Master Serverless LLM Apps with Amazon Bedrock

read time: 60 minutes
This free short course teaches how to deploy large language model-based applications using serverless technology with Amazon Bedrock. Learn to summarize audio files by pairing an LLM with an automatic speech recognition model, and deploy this audio summarizer as an event-driven serverless workflow using AWS Lambda.

Maximizing Generative AI with Customized LLMs

read time: 10 minutes

This article explores the importance of customizing Large Language Models (LLMs) for generative AI coding tools. It discusses three methods: Retrieval-augmented generation (RAG), In-context learning, and Fine-tuning. The article emphasizes the benefits of these methods in improving coding efficiency, information discovery, and collaboration between technical and non-technical teams.

Evaluating RAG Metrics Across Different Retrieval Methods

read time: 8 minutes
This article explores the evaluation of RAG (Retrieval-Augmented Generation) metrics across different retrieval methods. It discusses the performance of basic, Parent Document, and Ensemble retrievers in terms of context precision, faithfulness, answer relevancy, context recall, context relevancy, answer correctness, and answer similarity. The Ensemble Retriever, which combines sparse and dense retrievers, shows a balanced performance across multiple metrics, making it a robust choice for RAG applications.

Overcoming Challenges in Building Data-Driven Chat Applications with LLMs

read time: 15 minutes
Arcus discusses the challenges in building Large Language Model (LLM) chat applications that answer questions about proprietary data. They propose a solution that involves grounding LLMs on relevant data and using intelligent query transformations. This approach improves retrieval performance and LLM response accuracy. Read more about their solution here.

🧰 TOOLS

Design2Code: Transforming Web Designs into Code

read time: 3 minutes
Introducing Design2Code, an open-source tool that converts web design formats into clean, responsive HTML/CSS/JS code. Simply upload your design and let Design2Code generate the code for you. It supports various formats including sketches, wireframes, Figma, and XD. The project is built using Next.js and can be deployed with Vercel.

LangSmith: A Comprehensive Solution for LLM Application Development

read time: 15 minutes
LangSmith, a platform for Large Language Model (LLM) application development, monitoring, and testing, has announced its general availability. The platform supports workflows from prototyping to production, offering features like tracing, debugging, testing, comparison view, and A/B testing. It also provides a playground for rapid iteration and experimentation. LangSmith aims to solve common pain points in building with LLMs.

Reor: A Self-Organizing, AI-Powered Note-Taking App

read time: 5 minutes
Reor is an AI-powered desktop note-taking app that links related ideas, answers questions on your notes, and provides semantic search. It runs models locally, leveraging Llama.cpp, Transformers.js & LanceDB. It also supports OpenAI-compatible APIs. Learn more about Reor here.

Introducing LLMWare: A Unified Framework for Generative AI

read time: 8 minutes
LLMWare is an open-source unified framework for developing LLM-based application patterns, including Retrieval Augmented Generation (RAG). It provides an integrated set of tools for building industrial-grade, knowledge-based enterprise LLM applications. With support for multiple models, databases, and data stores, LLMWare makes it easy to develop and deploy generative AI applications. Check out the LLMWare GitHub repository for more information.

Embedchain: Build & Deploy AI Apps with Retrieval-Augmented Generation

read time: 3 min
Embedchain simplifies the creation of AI apps powered by Retrieval-Augmented Generation (RAG), making it easier for developers to leverage unstructured data in conversational AI/search applications. The framework can process your PDFs, docs, or web content making information searchable and supporting tasks like question answering. Get started quickly with their online demo and explore use cases on GitHub.

📰 NEWS & EDITORIALS

Introducing Sora: AI-Generated Video from Text

read time: 10 minutes
OpenAI introduces Sora, a text-to-video AI model capable of creating realistic scenes from textual instructions. Despite some limitations, Sora shows promise in generating complex scenes with multiple characters and motions. The model is currently being tested for safety and potential misuse before being integrated into OpenAI's products.

Introducing Google's Next-Generation AI Model: Gemini 1.5

read time: 15 minutes
Google has announced its next-generation AI model, Gemini 1.5. This model offers enhanced performance, a breakthrough in long-context understanding, and a highly efficient architecture. It can process up to 1 million tokens, enabling it to handle vast amounts of information, making it a powerful tool for developers and enterprises.

ChatGPT Introduces Memory Feature for Enhanced Conversations

read time: 7 minutes
OpenAI is testing a new memory feature in ChatGPT that allows it to remember details from past interactions, enhancing its conversational capabilities. Users can control what ChatGPT remembers and can turn off the memory feature at any time. The feature is currently being rolled out to a small group of users for testing.