Up to 70% of prompts in LLM applications are repetitive. Prefix caching can reduce inference costs by up to 90%, thus optimizing performance and saving money.
In this post, we will write a basic DNN using simple Python. To do that, we need to understand automatic differentiation and then implement it in code.
A brief introduction to Apache Cassandra for retrieval-augmented generation using Python and Ollama for developing applications free of cost locally or on a server.
Pydantic is a powerful Python library that uses type annotations to validate data structures. Learn about the powerful features of Pydantic with code examples.
February 3, 2025
by Vidyasagar (Sarath Chandra) Machupalli FBCS
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Minimize data loss and business disruption by implementing high availability and configuring disaster recovery for Loki with AWS S3 as the object store.
Learn how to build MCP servers to extend AI capabilities. Create tools that AI models can seamlessly integrate, demonstrated through an arXiv paper search implementation.
In this article, learn how to use LLMs for web scraping with ScrapeGraphAI, LangChain, and Pydantic. This guide covers setup, configuration, and data extraction
In some cases, one cannot store user-sensitive data permanently. Let's create a simple application that handles sensitive data leveraging Spring and Redis.
Article explains how Aspect-Oriented Programming (AOP) simplifies modern app development by handling cross-cutting concerns like logging, security, and performance seam
Guará is the Python implementation of the design pattern Page Transactions. It focuses on the transactions a user can perform on an application, such as Submit Forms.
Practical tips for debugging front-end network issues using browser developer tools, with insights on request replay, throttling, and storage management.
Explore how to easily build cloud-deployed microservices using serverless technology with Azure Functions and connect with APIs standardized in API Management.
This article discusses building an efficient ML pipeline with PySpark, covering data loading, preprocessing, model training, and evaluation for large datasets.
We'll discuss SmartXML, an XPath alternative for parsing complex XML files, converting them to SQL, and loading the results into a database seamlessly.
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