How to Build AI Voice Apps in 2024

📖 TUTORIALS & CASE STUDIES

Build Multimodal Search with Amazon OpenSearch Service

Read Time: 15 minutes

This article demonstrates how to leverage Amazon OpenSearch Service and the Amazon Bedrock Titan Multimodal Embeddings model to build a rich multimodal search application that seamlessly integrates both text and visual information. The guide walks through the end-to-end process, including ingesting a retail dataset, generating embeddings, and running multimodal search experiments. The article showcases the flexibility and performance benefits of combining text and image inputs for more precise and relevant search results.

How to Build AI Voice Apps in 2024

Read Time: 17 minutes

This article explores the rapidly evolving landscape of building AI-powered voice applications. It covers key aspects like leveraging OpenAI's Whisper STT model, using WebRTC for reliable audio streaming, and integrating text-to-speech services. The author shares insights on overcoming common challenges like voice activity detection, and highlights emerging frameworks and managed services that can accelerate voice app development for software developers.

Tutorial: Get Started with the Gemini API

Read time: 10 minutes

This tutorial covers how software developers can use the Gemini API to leverage Google's large language models. It demonstrates generating text from text and multimodal inputs, conducting multi-turn conversations, using embeddings, and understanding advanced features like safety settings and generation configuration. This comprehensive guide equips developers with the knowledge to integrate powerful generative AI capabilities into their applications.

Building AI Projects with DuckDB: A Powerful Open-Source Database

Read time: 12 minutes

This article explores how software developers can leverage DuckDB, a modern, high-performance, in-memory analytical database, to build powerful AI applications. It covers integrating DuckDB with Retrieval Augmented Generation (RAG) frameworks and using it as an AI query engine to analyze data using natural language. Developers will learn to set up DuckDB, load data, query the database, and create innovative AI projects that combine the power of DuckDB and large language models.

AI-Powered Assistants for Investment Research with Multi-Modal Data: An Application of Agents for Amazon Bedrock

Read time: 15 minutes

This article describes how Agents for Amazon Bedrock can be used to build AI-powered assistants that help financial analysts leverage structured and unstructured data for investment research. The agents can orchestrate interactions between language models, APIs, databases, and knowledge bases to provide insights and recommendations based on prompts. This demonstrates the value of generative AI agents in automating tasks and amplifying the productivity of financial analysts. 

🧰 TOOLS

Fabric: An Open-Source Framework for Augmenting Humans with AI

Read time: 13 minutes

Fabric is an open-source framework that provides a modular system for leveraging generative AI to solve specific problems. It features a library of "Patterns" - pre-made AI prompts that can be easily integrated into software applications. Fabric aims to address the "integration problem" of AI by enabling developers to easily incorporate powerful AI capabilities into their tools and workflows.

Evaluate Prompts in the Developer Console

Read time: 12 minutes

This article introduces new features in the Anthropic Console that make it easier for developers to generate, test, and evaluate prompts for their AI-powered applications. With the ability to generate test cases, compare model responses, and get feedback from subject-matter experts, developers can quickly iterate and improve the quality of their prompts, resulting in better outcomes for their users.

An AI Agent that Writes (Actually Useful) Code for You

Read time: 10 minutes

Micro Agent is a focused AI tool that helps software developers write and fix code by automatically generating tests and iterating on code until all tests pass. Unlike general-purpose coding agents, Micro Agent is designed to do one task well—generate code that meets a specific test case. This tool leverages large language models to streamline the code-writing process, allowing developers to stay competitive in the market by automating repetitive coding tasks.

Quality Prompts: Boosting LLM Performance with 58 Powerful Prompting Techniques

Read Time: 6 minutes

Quality Prompts is a Python library that implements 58 prompting techniques to help software developers quickly use and evaluate different prompting methods for their large language model (LLM) applications. The library allows you to write prompt components, leverage relevant few-shot examples, and apply techniques like System2Attention and Tabular Chain of Thought to boost the accuracy and capabilities of your LLMs. With this tool, developers can easily experiment with prompting and find the most effective techniques for their needs.

Introducing AuraFlow v0.1, an Open Exploration of Large Rectified Flow Models

Read time: 9 minutes

AuraFlow, an open-source text-to-image generation model, is a reaffirmation of the open-source AI community's resilience. Developed through a collaboration between researchers, it showcases technical advancements like optimal learning rate transfer, improved training efficiency, and prompt-enhancement capabilities. The article invites the developer community to experiment with AuraFlow, contribute to its development, and leverage it as a foundation for further innovations in generative AI.

📰 NEWS & EDITORIALS

Agent Dev & The Case for The Engineer's Creative Process

Read time: 7 minutes

This article explores how building agent infrastructure for generative AI is a non-linear, creative process that requires engineers to embrace an artistic mindset. It highlights the importance of developing a relationship with the work, understanding agent memory and user interactions, and leveraging a creative process to navigate the uncertainty and potential failures inherent in this new frontier of development.

FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low precision

Read time: 8 minutes

FlashAttention-3 introduces techniques to speed up attention on Hopper GPUs, including exploiting asynchrony to overlap computation and data movement, and leveraging FP8 low precision to achieve up to 1.2 PFLOPS performance. These improvements enable more efficient GPU utilization, better performance with lower precision, and the ability to use longer context in large language models, all crucial for software developers building AI-powered applications.

LlamaCloud—Built for Enterprise LLM App Builders

Read time: 10 minutes

LlamaCloud is a new platform designed to streamline the development of production-ready Retrieval Augmented Generation (RAG) and Agent-based applications leveraging Large Language Models (LLMs). It addresses common challenges like data quality, scalability, and configuration complexity, offering features like managed data ingestion, advanced retrieval capabilities, and an interactive UI for rapid iteration. LlamaCloud aims to help developers spend less time setting up their data pipelines and focus more on building innovative LLM applications.

How Good Is ChatGPT at Coding, Really?

Read time: 6 minutes

This IEEE Spectrum article explores a study that evaluated the code produced by OpenAI's ChatGPT, finding it can be quite capable but also struggles due to training limitations. While ChatGPT excelled at older coding problems, its performance dropped significantly for newer challenges, indicating it lacks the critical thinking skills of a human programmer. The researchers provide insights on how developers can best leverage ChatGPT to complement their own coding abilities.