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Brief

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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TOOL · dev.to — MCP tag English(EN) ·

    I built a self-hosted RAG system for Journalism — What Production Retrieval Taught Me

    A developer built Atlas, a self-hosted Retrieval-Augmented Generation (RAG) system tailored for journalism, utilizing local models and PostgreSQL with pgvector. The system ingests RSS feeds, embeds content, and provides features like grounded Q&A, claim-level fact-checking, and story brief generation. Key lessons learned include the necessity of hybrid search combining vector and full-text search for news corpora, and the significant performance gains from batch embedding over individual article embedding. AI

    IMPACT Highlights the practical challenges and solutions in deploying RAG for specialized domains like journalism, emphasizing hybrid search and efficient embedding strategies.

  2. TOOL · dev.to — LLM tag English(EN) ·

    Building Agentic Laravel Apps with Prism PHP

    A new guide details how to build agentic applications using Prism PHP within the Laravel 13 framework. Prism PHP extends Laravel's first-party AI SDK by enabling multi-provider tool calling, agentic loop control, and RAG pipelines. The guide emphasizes configuring AI providers abstractly to allow for easy switching between services like OpenAI, Gemini, and Anthropic, and provides examples for basic text generation and more complex tool-calling agents. AI

    Building Agentic Laravel Apps with Prism PHP

    IMPACT Enables developers to build more sophisticated AI agents within the Laravel ecosystem by abstracting complex provider interactions.

  3. TOOL · dev.to — LLM tag English(EN) · · [2 sources]

    Stop Spinning Up Separate Vector DBs: Multi-Tenant Spring AI with Pgvector Metadata Filtering

    This article proposes a multi-tenant solution for Spring AI applications using Pgvector, a PostgreSQL extension for vector embeddings. It advocates for logical tenant isolation through metadata filtering within a shared Pgvector store, rather than provisioning separate databases per tenant. The approach leverages Spring Security to inject tenant context into Spring AI's filter expressions, ensuring secure data segregation and improved performance by indexing metadata fields. AI

    IMPACT Provides a practical solution for securely scaling RAG applications by enabling multi-tenancy with existing database infrastructure.

  4. RESEARCH · dev.to — MCP tag English(EN) · · [5 sources]

    Understanding Embeddings easily.

    Embeddings are a core concept in AI, transforming text and other data into numerical representations that capture meaning. These numerical vectors allow AI models to understand relationships between words and concepts, enabling functionalities like semantic search and Retrieval-Augmented Generation (RAG). While vector databases like Pinecone, Weaviate, and Chroma are commonly used for storing and querying these embeddings, alternative approaches like BM25 retrieval with tools such as Meilisearch can also be effective for specific use cases, offering simpler operation and lower costs. AI

    IMPACT Understanding embeddings is crucial for developing and utilizing advanced AI applications like semantic search and RAG systems.

  5. TOOL · AssemblyAI blog English(EN) · · [3 sources]

    Build an AI voice agent for customer support that can look up orders

    AssemblyAI has released a tutorial for building an AI voice agent capable of handling customer support tasks like order lookups and account verification. The agent utilizes AssemblyAI's Voice Agent API, which integrates speech-to-text, LLM reasoning, and text-to-speech on a single WebSocket connection to provide a seamless customer experience. Separately, a developer documented a process for training a support AI using real customer service chat logs, employing Retrieval-Augmented Generation (RAG) with a vector store and hybrid search to extract knowledge from historical conversations. AI

    IMPACT Provides practical examples of deploying AI for customer support and knowledge retrieval, showcasing specific tools and techniques.

  6. RESEARCH · Towards AI English(EN) · · [56 sources]

    Building RAG Systems: A Complete Guide

    Retrieval-Augmented Generation (RAG) systems are a crucial technique for enhancing Large Language Models (LLMs) by allowing them to access and utilize external, up-to-date information. RAG addresses LLM limitations such as knowledge cutoffs and context window limits by retrieving relevant data before generating a response. This approach is distinct from fine-tuning, which modifies the model's behavior rather than its knowledge base. Building a RAG system involves two main pipelines: an ingestion pipeline for preparing and storing data, and a retrieval pipeline that fetches context for each user query. AI

    Building RAG Systems: A Complete Guide

    IMPACT Enables LLMs to provide more accurate, up-to-date, and domain-specific answers by integrating external knowledge bases.