Today's AI applications predominantly use a standard chat interface. While this approach is simple to implement, it creates unnecessary interaction overhead, limits human scalability, and fails to harness the full potential of LLMs.
MarketMind is a small demo Django-based web application that integrates with a language model agent to handle search queries about companies. The application provides a summary of the latest news and stock price for a given company using LLM-based agent.
This guide focuses on setting up Amazon Bedrock, a serverless AI service by AWS. It covers account setup, AWS CLI configuration, accessing Bedrock, and generating a model response, including a hands-on example and troubleshooting tips.
This post guides you through using Amazon Bedrock's Large Language Model to transcribe an audio file into text. It includes setting up an AWS environment, uploading the audio to Amazon S3, and transcribing it using Amazon Transcribe, along with tips on formatting transcripts and crafting prompts.
This post is a concise guide on using Amazon Bedrock to transcribe, summarize, and log audio conversations. It covers AWS setup, Amazon S3, Amazon Transcribe, Large Language Model of Bedrock, and logging with Amazon CloudWatch.
This guide explains how to use AWS Lambda for serverless architecture, where a new audio file in an S3 bucket triggers a transcription request to Amazon Transcribe, then the result is summarized by Amazon Bedrock.
The post provides a comprehensive guide on how to implement an application for generating unique restaurant names using Large Language Models (LLMs) and the LangChain framework. It covers setting up the environment ...
This is an introductory article detailing the steps to set up a development environment for a stock exchange system using ASP.NET Core 6 and Vue.js...
This article This one discusses the integration of real-time stock data into an application using IHttpClientFactory, a tool that manages HttpClient instances...
The blog post details the process of deploying the app to Microsoft Azure. The process involves encapsulating the application within a Docker container, creating a Docker image of the application, and using Visual Studio tools for developing with Docker containers...