#dense retrieval
207 related articles
·3 min
A Guide to Selecting Datasets for RAG Experiments: Evaluating Dense, Sparse, and Hybrid Retrieval
A systematic guide to standardized datasets for RAG retrieval experiments, covering BEIR, MS MARCO, Natural Questions, and TREC benchmarks for dense, sparse, and hybrid retrieval evaluation.
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·3 min
PDF Document Auto-Classification in Practice: Why Embedding Models Fall Short and Better Alternatives
Analysis of why embedding models (like bge-m3) fail at PDF document classification, covering label sensitivity and semantic dilution issues, with three better approaches: LLM classification, supervised classifiers, and multimodal feature fusion.
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·3 min
Building a Custom Switch Management Interface with Gemini: AI-Powered Network Operations in Practice
A Homelab user leveraged Gemini AI to build a custom web management interface for an HP switch via REST API, enabling port control, PoE management, and connection visualization.
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·3 min
Kimi K3 Launch: A 2.8 Trillion Parameter Flagship Model Makes Its Grand Entrance
Moonshot AI launches Kimi K3 with 2.8 trillion parameters and 1M token context. Google delays Gemini 3.5 Pro, AI coding tools upgrade collectively as competition shifts to coding and Agent capabilities.
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·3 min
Replacing spaCy's Sentencizer with yasbd: Accuracy Jumps from 55% to 98.9%
spaCy's default Sentencizer achieves only 55.4% accuracy on edge cases, while open-source library yasbd reaches 98.9%. Analysis of limitations and integration code examples.
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·3 min
Deep Dive into the Kimi-K3 Technical Report: A Comprehensive Analysis of Architecture, Long-Context Processing, and Reasoning Capabilities
Deep dive into Moonshot AI's Kimi-K3 technical report, analyzing its long-context processing, MoE architecture, reasoning improvements, and its position in global AI competition.
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·3 min
Deep Dive into the Kimi-K3 Technical Report: Architecture, Long Context, and Reasoning Capabilities
Deep analysis of Moonshot AI's Kimi-K3 technical report covering long context processing, MoE architecture, reasoning capabilities, and China's position in the global AI competition.
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·4 min
Robot Valet Parking at Gatwick Airport: How the AGV Automated Parking System Works
London Gatwick launches UK's first robot valet parking using AGV technology, boosting parking capacity by 30-60%. Learn how the system works, its challenges, and the business case for automated parking.
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·3 min
Understand Anything: The AI Skill That Turns Code into Interactive Knowledge Graphs
Understand Anything is a high-star open-source GitHub skill that runs static analysis on any codebase and generates interactive knowledge graphs. It supports Claude Code, Cursor, Copilot and other agents, letting engineers ask questions in natural language with path references.
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·3 min
Understand Anything: The AI Skill That Turns Code into an Interactive Knowledge Graph
Understand Anything is a high-star open-source GitHub skill that performs static analysis on any codebase to generate an interactive knowledge graph, supporting Claude Code, Cursor, Copilot and more.
Read more →
·3 min
Understand Anything: The AI Skill That Turns Code into an Interactive Knowledge Graph
Understand Anything is a high-star open-source GitHub skill that performs static analysis on any codebase to generate interactive knowledge graphs, supporting Claude Code, Cursor, Copilot and more.
Read more →
·3 min
DeepSeek-OCR Deployment and Fine-Tuning: A Complete Hands-On Guide
Master the full DeepSeek-OCR deployment and fine-tuning workflow: vLLM inference deployment, efficient Unsloth fine-tuning, dataset preprocessing, LoRA training, validation, and RAG vector database integration.
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·3 min
Context Engineering: Why Does More Context Make AI Dumber?
Anthropic introduces Context Engineering, revealing Context Rot: the more tokens in the window, the worse AI retrieval accuracy. Learn the three principles, just-in-time retrieval, and three moves against context overflow.
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GitHub Daily · July 24: Agentic Tools …
·3 min
GitHub Daily · July 24: Agentic Tools Blossom Everywhere, From CMS to Databases Embracing AI
GitHub Trending July 24: Agentic capabilities go from concept to standard, with Instatic and Chat2DB embedding AI deeply into CMS and database clients.
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Mindwalk: Replaying AI Coding Agent Be…
·4 min
Mindwalk: Replaying AI Coding Agent Behavior on a 3D Code Map
Mindwalk renders codebases as 3D maps, visually replaying the full operation trajectories of AI coding agents like Claude Code and Cursor. A deep dive into its core ideas, use cases, and the future of agent observability tools.
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·3 min
Codex Beginner's Guide: A Step-by-Step AI Work Assistant Tutorial for Non-Coders
Complete beginner's guide to OpenAI Codex desktop client: installation, setup, project management, and plugins — no coding required. Let AI actually do work for you.
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·8 min
LangChain Crash Course: A Complete Roadmap from Zero to Agent Development
A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.
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·7 min
Context Engineering: A Systematic Approach to Stop AI Agents from 'Talking Nonsense'
Context engineering is the core methodology for building efficient AI Agents, covering query enhancement, RAG retrieval, prompt design, memory management, and tool invocation. Master Write, Select, Compress, and Isolate to solve LLM hallucination at its root.
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·6 min
Vue3 from Scratch: A Hands-On Journey to Building an AI Health Monitoring System
A Vue3 beginner tutorial centered on "learn just enough, apply immediately." A three-stage path covers reactivity, Composition API, Element Plus, and data visualization, culminating in an enterprise-grade AI health monitoring system with blood sugar management, RAG consultation, and doctor-patient collaboration.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Topics in the Agent Domain
A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.
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