#vector semantic routing
200 related articles
Intelligent Model Routing: The Core Te…
·3 min
Intelligent Model Routing: The Core Technology and Implementation Guide for AI Cost Reduction and Efficiency
Intelligent Model Routing is becoming key AI infrastructure. This article explores its principles, solution types, technical challenges, and implementation considerations to help developers balance cost, latency, and quality.
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·4 min
jlens-gguf: Bringing Jacobian Lens Visualization and Steering to llama.cpp Local LLMs
jlens-gguf is an open-source tool bringing Anthropic's Jacobian Lens interpretability method to GGUF and llama.cpp, enabling internal observation, real-time steering, and abliteration for both dense and MoE models.
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·4 min
Spring AI 1.0 Practical Guide: A Java Developer's Fast Track to Building AI Applications
Spring AI 1.0 is here — Java developers can now build AI apps without switching to Python. This guide covers LLM integration, RAG, intelligent customer service, and Agent patterns for enterprise deployment.
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·5 min
From Vibe Coding to AI Engineering: A Complete Guide to Enterprise-Grade Development
In the AI programming era, Vibe Coding alone can only build toys. This article deeply analyzes the complete engineering path from Vibe Coding to SDD spec-driven development, covering Claude Code and Codex tool selection, the SuperPower plugin, and domestic LLM comparisons.
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·7 min
DeepSeek's Mysteriously Retracted Paper: Breaking AI's Multimodal Reasoning Bottleneck with Visual Primitives
DeepSeek's paper 'Thinking with Visual Primitives' was online for just 4 hours before being pulled. It uses bounding boxes and points as reasoning primitives, letting models 'point at' images to outperform GPT, Gemini, and Claude on maze navigation and counting.
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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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·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
From Chat to Agent Platform: A Practical Guide to Engineering AI Applications
A deep dive into engineering AI applications: from a simple chat page to a multi-layer Agent platform, covering RAG knowledge bases, Workflow scheduling, multi-model management, and run tracing.
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·6 min
30-Minute Hands-On: Building a Working AI Agent from Scratch
Skip the dry theory and get hands-on! This article demonstrates step by step how to build a working AI Agent from scratch in 30 minutes using AI coding tools—covering the agent skeleton, tool system, memory mechanism, Flask web UI, and DeepSeek API integration.
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·3 min
AI Agent Development Landscape: From Architecture Design to Production Deployment
A systematic overview of the AI Agent tech stack: RAG retrieval, Agent planning, MCP protocol, AI Gateway, and observability — helping developers build production-grade AI systems.
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Open Deep Research: A Complete Guide t…
·2 min
Open Deep Research: A Complete Guide to LangChain's Open-Source AI Research Agent
A deep dive into LangChain's open-source project open_deep_research: an AI deep research agent built on LangGraph, supporting flexible multi-model and multi-search tool configuration, with 12,000+ stars.
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·4 min
UK Sovereign AI: How Cosine AI Is Punching Above Its Weight Against American Compute Dominance
Cosine AI founder reveals how the UK's first sovereign LLM is being built — from government compute grants and RL credit attribution to multi-agent orchestration and synthetic data pipelines.
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·4 min
7 Core Components for Designing Long-Running AI Agents
A deep dive into the 7 core components for building long-running AI Agents: Goal, Evaluator, Verifier, Loop, Orchestration, Observability, and Memory.
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·3 min
Enterprise RAG Knowledge Isolation in Practice: Physical Isolation vs. Adaptive Soft Boundaries
A deep dive into two enterprise RAG knowledge isolation strategies: physical isolation vs. adaptive soft boundaries — covering metadata tagging, dynamic user-profile filtering, hybrid retrieval architecture, and data quality best practices.
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·4 min
Complete Guide to Running Claude Code on Amazon Bedrock
Complete guide to deploying Claude Code on Amazon Bedrock: environment setup, cross-region inference, prompt caching, cost attribution, Guardrails, and the Mantle endpoint for enterprise compliance.
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·4 min
Understanding Transformer From the Ground Up: How Language Models Predict the Next Word
From Tokenization and Embedding to the Attention mechanism, this article systematically breaks down how Transformer works — and how ChatGPT turns input text into next-token probabilities.
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GitHub Daily · July 20: AI Agent Infra…
·3 min
GitHub Daily · July 20: AI Agent Infrastructure Explodes Across the Board
AI Agent infrastructure explodes across GitHub Trending: OmniRoute unifies 268+ providers, cognee adds long-term memory, and self-hosted openship tops growth with +1719 stars.
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·2 min
14 PDF Parsers Compared: Semantic Fidelity Is the Real Bottleneck for RAG Applications
A benchmark of 14 PDF parsers focused on Meaning Survival, not just character accuracy. Covers GPT, Mistral OCR, Azure DI, and key insights for RAG pipeline optimization.
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·2 min
Heap Code: The Open-Source VS Code AI Coding Assistant That Truly Supports Local Models
Heap Code is an open-source VS Code extension supporting local models via Ollama and LM Studio, plus OpenAI-compatible APIs. Features completions, chat, inline edit, and agent mode — zero telemetry, no account required.
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·2 min
Agent Skill Hit Rate Dropping? 4 Methods to Solve the Skill Routing Problem
Skill hit rate dropping as your Agent scales? Learn how Progressive Disclosure works and explore 4 solutions: better descriptions, Skill Trees, negative samples, and recall + rerank.
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