Daily Dose of Data Science | Avi Chawla | Substack·1yAI Agent Crash Course—Part 1
In this crash course, learn about AI agents and their implementation. It covers the fundamentals, memory for agents, agentic flows, guardrails, implementing agentic design patterns, and optimizing agents for production. The aim is to build autonomous systems that can reason, plan, take actions, and correct themselves, going beyond the capabilities of standalone generative models.
Fireship·1yGoogle finally shipped some fire…
Google has released Gemini 2.0, a new large language model, which offers significant advantages over competitors with its real-world use cases, accuracy, and cost-effectiveness. Despite past challenges, Gemini 2.0 provides substantial improvements, including processing large volumes of data more efficiently and at a lower cost. It features various models for different needs, including free chatbot access, creating versatility for developers and users alike.
Tinybird·1yBuild a Datadog alternative in 5 minutes
A free Next.js app and Tinybird backend can be deployed in less than 5 minutes as a simple Datadog alternative for log analytics. This guide covers the stack, development workflow, instrumentation, data handling, and API setup. A focus is placed on building a basic, functional version to iterate quickly, with optimizations for scale coming later. Detailed instructions provide insight into using mock data for testing, integrating APIs with Next.js, and deploying using CI/CD processes.
Machine Learning Mastery·1y10 Useful LangChain Components for Your Next RAG System
LangChain is a robust framework designed to simplify the development of LLM-powered applications, particularly useful for building retrieval augmented generation (RAG) systems. The post outlines 10 key components of LangChain, such as document loaders, text splitters, embeddings, vector stores, retrievers, LLM wrappers, chains, memory usage, interaction tools, and evaluation tools. These components facilitate data ingestion, text processing, similarity-based search, and interaction with external systems. A simplified Python example demonstrates their use in a question-answering workflow.
Shaaf·1yJava + LLMs: A hands-on guide to building LLM Apps in Java with Jakarta
Java continues to be a dominant programming language used in various critical applications. The rise of Large Language Models (LLMs) opens new opportunities for Java developers. Despite the common perception that Python is necessary for LLM implementation, Java, with tools like LangChain4J, is equally capable of integrating LLMs through REST APIs. Resources, including a step-by-step guide and source code, are available for developers to explore this integration.
Medium·1yBuilding Next-Gen AI Agents: Evolving Patterns and Best Practices
Generative AI is revolutionizing industries by enabling the creation of intelligent, self-learning systems. This blog delves into the evolving patterns and best practices for developing scalable AI agents. It covers topics such as direct prompting, embeddings, evaluations, and fine-tuning. Examples include AI chatbots for e-commerce and Java code generation. The blog also highlights evaluation techniques like automated, human, and self-evaluation to ensure reliable AI performance.
Vaadin·1yAdvanced RAG techniques with Spring AI
Spring AI offers advanced features for Retrieval Augmented Generation (RAG) to enhance LLM responses with domain-specific knowledge. These features include document loading using TikaDocumentReader, advanced query processing with MultiQueryExpander and CompressionQueryTransformer, and custom document processing via RetrievalAugmentationAdvisor. Error handling and safety measures, like SafeguardAdvisor, can be implemented to ensure robust, production-ready applications.
Sebastian Raschka·1yUnderstanding Reasoning LLMs
In 2024, the field of large language models (LLMs) saw significant specialization, including the development of reasoning models designed to excel at complex tasks like puzzles, advanced math, and coding challenges. This post discusses four main approaches to building reasoning models: inference-time scaling, pure reinforcement learning (RL), supervised fine-tuning combined with reinforcement learning (SFT + RL), and model distillation. It highlights DeepSeek-R1 as a leading example of a reasoning model and compares its methodologies and efficiency with OpenAI's o1. The value of developing smaller, distilled models on a limited budget is also emphasized, presenting a cost-effective alternative for researchers and engineers.
Tech With Tim·1yHow To Build an API with Python (LLM Integration, FastAPI, Ollama & More)
Learn how to build a simple Python API to control access to an LLM (large language model) like GPT using FastAPI. Discover the importance of securing API keys, setting up dependencies, and controlling API access to prevent unauthorized usage and curb costs. The guide walks you through running the LLM locally, setting up an API endpoint, and integrating API key authentication.
Medium·1yPrompt chaining is dead. Long live prompt stuffing!
Prompt chaining was once essential for working within limited context windows in LLMs, allowing the creation of complex and nested JSON objects. However, with the advent of modern LLMs like Gemini 2.0, which offer large context windows, prompt chaining has become obsolete. Instead, prompt stuffing—putting all necessary context into a single prompt—saves time and resources, significantly reducing API costs and complexity. This shift reflects the rapid evolution in the AI field, where practices can quickly become outdated.
Build With GenAI·1yKlarity – Open-source tool to analyze uncertainty/entropy in LLM output
Klarity is an open-source tool designed to analyze uncertainty and decision-making in LLM token generation. It provides real-time analysis, combining log probabilities with semantic understanding, and generates structured JSON outputs. The tool supports Hugging Face transformers and is customizable for different models.
