#vector quantization
285 related articles
·2 min
AI Large Model Development Learning Path: A Complete Guide from Zero to Enterprise Projects
A complete learning roadmap for AI large model development — covering Transformer, Prompt Engineering, RAG, LangChain, Agent development, fine-tuning, and deployment.
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·3 min
Programmer's Guide to the AI Era: Application Development Is a Real Opportunity for Ordinary Developers
Too hard to become an algorithm engineer? Too basic to just use AI tools? This guide breaks down the three levels of AI adoption for programmers, with a focus on Agent development and large model engineering — including salaries, timelines, and window risks.
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·4 min
Tencent Marvis Hands-On: How Multi-Agent AI Takes Over Your Computer
Deep dive into Tencent's Marvis AI agent: multi-agent architecture, intelligent file organization, document analysis, cross-device sync, and local privacy mode explained.
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·7 min
AI Agent Development: A Four-Stage Competency Model from 15K to 40K RMB Salary Growth Path
A detailed four-stage competency model for AI Agent development: from Python/RAG basics (15K) to workflow orchestration (20K), inference optimization (30K), and Agent cluster governance (40K RMB).
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·7 min
GPT Image 2 Hands-On Review: In-Depth Evaluation of Text Layout and Character Breakdown Capabilities
GPT Image 2 hands-on review: near-flawless poster text layout and automatic character breakdown with Chinese annotations. Deep analysis of core capabilities, comparison with Nano Banana, and risk assessment for access channels.
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·7 min
AI Agent Memory Layer Comparison: Mem0, Zep, Letta, and Cloudflare — Four Major Solutions Evaluated
In-depth comparison of four AI agent memory layer solutions: Mem0's extract-retrieve approach, Zep's temporal knowledge graphs, Letta's self-editing memory, and Cloudflare Durable Objects as infrastructure primitives.
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·8 min
The Enterprise AI Flywheel: Building a Model-Eval Loop as a Competitive Moat
The core of enterprise AI isn't calling general models—it's building a self-reinforcing "model-harness-sandbox-eval" flywheel. This article analyzes the four components, tacit knowledge moats, and the "token value per watt" efficiency metric.
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·4 min
Dify Beginner's Guide: Interface Overview & Complete Docker Self-Hosted Deployment Guide
Complete guide to Dify's four core modules (Explore, Studio, Knowledge Base, Tools) with step-by-step Docker self-hosted deployment instructions for building production-grade AI apps.
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·3 min
Complete RAG System Development Guide: Data Preprocessing, Vector Retrieval, and Output Optimization in Practice
A complete guide to building RAG systems: covering data preprocessing, vector databases, embedding models, hybrid search, re-ranking, and advanced topics like Graph RAG and multimodal RAG.
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·4 min
Building an Enterprise Knowledge Base with RAG: A Complete Guide from Principles to Optimization
A comprehensive guide to building enterprise knowledge bases with RAG, covering vector database selection, text chunking, Embedding models, multi-strategy retrieval, re-ranking, and Agent integration for high-accuracy AI Q&A systems.
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·3 min
AI Large Language Model Learning Roadmap from Scratch: From Beginner to Enterprise-Level Practice
A systematic AI LLM learning roadmap from scratch, covering Python basics, Prompt Engineering, RAG, Agent development, and enterprise-level projects.
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·3 min
AI Large Language Model Zero-to-Hero Systematic Tutorial: A Complete Guide from Basics to Practice
A comprehensive 748-episode AI LLM tutorial covering Transformer architecture, Prompt Engineering, RAG, Agent, fine-tuning, and enterprise projects like AI customer service and knowledge bases.
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·4 min
AI Large Language Model Career Transition Roadmap: A Three-Phase Learning Guide from Zero to Enterprise-Ready
A systematic three-phase AI LLM career transition roadmap: from Transformer fundamentals to RAG, Agent & LangChain development, to LoRA fine-tuning. Build enterprise-ready skills in two months.
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·3 min
The Ultimate Ollama Guide: A Complete Walkthrough for Running AI Models Locally
Complete Ollama guide: install and run open-source LLMs like DeepSeek, Llama, and Qwen locally on Windows/Mac/Linux. Free, private, and beginner-friendly.
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From a Single Prompt to an AI Product:…
·3 min
From a Single Prompt to an AI Product: Starting Is Easy, Shipping Is Hard
AI startups begin with a prompt, but going from idea to product means overcoming major technical, product, and business challenges. A low barrier to entry doesn't mean a low barrier to success.
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·3 min
748-Episode AI Large Language Model Development Tutorial: Seven Core Modules from Zero to Project Deployment
Analysis of a 748-episode, 198-hour AI LLM development tutorial covering API integration, prompt engineering, RAG, AI Agents, fine-tuning, multimodal development, and deployment.
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·3 min
The Complete Skill Map for AI Application Engineers: A Progressive Roadmap from Fundamentals to Production
A systematic breakdown of the complete skill structure for AI application engineers, covering Python & deep learning fundamentals, small model engineering, LLM fine-tuning, Agent development, and enterprise projects.
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·3 min
Gemini 5.2 in Claude Code: Real-World Testing — Does It Crush Opus on Cost-Effectiveness?
Real-world testing of Gemini 5.2 in Claude Code vs Opus across web design, coding, creative tasks, and Storm research — analyzing the open-source model's cost advantage and ideal use cases.
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·3 min
GML 5.2 Multimodal Upgrade Hands-On: Full Validation with DeepSeek V4
Hands-on testing of GML 5.2 and DeepSeek V4 multimodal upgrades on OneBlockBase, covering vision-text workflows, safety mechanisms, and deployment tips.
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·3 min
AI Large Language Model Learning Roadmap: A Systematic Path from Zero to Project Implementation
A detailed AI LLM learning roadmap covering Transformer architecture, Prompt Engineering, RAG, Agent development, model fine-tuning & deployment, with enterprise project guides.
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