#basic research
1600 related articles
Tech Frontiers·2 min
Qwen Core Team Turmoil, OpenAI and Google Release New Models in Rapid Succession | AI Daily
Multiple core leaders depart Alibaba's Qwen team amid metric disputes. Same day: MiniMax Music 2.5+, OpenAI GPT 5.3 Instant, Google Gemini 3.1 Flashlight, and Seedance 2.0 pricing announced.
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Tech Frontiers·2 min
GLM5 Architecture Leaked: 745B Parameters, DeepSeek V4 May Launch Quantized Smaller Model First
GLM5 code leak reveals 745B-parameter MoE architecture replicating DeepSeek V3. DeepSeek V4 may launch a 200B quantized model first, with flagship exceeding 1T parameters.
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Tech Frontiers·3 min
GPT-5.3 Codenamed "Garlic" Coming Soon, Claude Cowork Launches Targeting Non-Developers
OpenAI's GPT-5.3 codenamed Garlic is coming soon, Anthropic launches Claude Cowork for non-developers, plus breakthroughs in Baichuan M3 medical and SiNong agricultural AI models.
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Tutorials·3 min
The Complete Guide to Claude Code: From Personal Assistant to AI Agent Development
A systematic breakdown of the Complete Guide to Claude Code course, covering context engineering, MCP protocol, claude.md configuration, multi-Agent architecture, and three progressive projects.
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Tutorials·3 min
Complete Breakdown of Andrew Ng's Agent Course: A Practical Guide to the Five Core Modules of AI Agent Development
Deep dive into Andrew Ng's viral AI Agent course covering five core modules: Reflection, Planning, Tool Use, Multi-Agent Collaboration, and Memory, with practical learning paths for LLM agent development.
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Tutorials·3 min
From Traditional RAG to Agentic RAG: Principles Compared and Enterprise-Grade Implementation Guide
Deep dive into traditional RAG limitations and Agentic RAG upgrades, with ChatBox source code analysis covering core tool design, intelligent decision flows, and LangGraph implementation for enterprise deployment.
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Tutorials·3 min
LangChain Core Concepts Explained: A Complete Guide to Components, Chains, and Agents
Deep dive into LangChain's three core concepts—Components, Chains, and Agents. Learn how this open-source framework connects LLMs to the external world and helps developers build enterprise AI apps.
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Tutorials·3 min
RAG Technology Full-Stack Analysis: Core Principles, Enterprise Implementation & Learning Path
Deep analysis of RAG technology's core principles, three key values, enterprise implementation cases, common pitfalls, and a systematic learning roadmap covering vector databases, retrieval optimization, and Knowledge Graph fusion.
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Tutorials·3 min
Enterprise RAG Implementation: Architecture Principles and Production-Grade Optimization Guide
Complete guide to enterprise RAG architecture covering data indexing, vectorization, and retrieval optimization. Practical insights on chunking strategies, hybrid retrieval, and hallucination control for production-grade LLM applications.
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Tutorials·3 min
Getting Started with LLM Application Development from Scratch: A Complete Guide to Learning Paths and Career Directions
A complete beginner's guide to LLM application development: learn the three key directions (API calling, RAG, Agent), master frameworks like LangChain, and follow a step-by-step learning path to become an AI application developer.
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Tutorials·3 min
Learning LLM Application Development from Scratch: A Complete Roadmap from RAG to Agent
How to start LLM application development from scratch? A complete roadmap covering Python basics, RAG knowledge bases, and Agent development with LangChain.
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Product Reviews·3 min
Local Deployment of Qwen 3.6 27B on 4×3080Ti: Real-World Coding Test with OpenCode
Real-world test of Qwen 3.6 27B FP8 deployed on 4×3080Ti 16GB modded GPUs with OpenCode for system tool development. Covers hardware setup, inference speed, context management, and productivity gains.
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Tutorials·3 min
Enterprise AI Agent Four-Layer Architecture Design and PDCA Continuous Optimization Practical Guide
Deep dive into enterprise AI Agent four-layer architecture design (User, Gateway, Agent Service, Capability layers) with PDCA optimization methodology and dual manual+automated evaluation for production-grade Agent systems.
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Tutorials·2 min
Running Out of Codex Credits? AnySearch Skill Saves You Massive Token Overhead
Real-world testing shows AnySearch Skill saves ~27% Token overhead for Codex while significantly improving search quality. Learn how it works, how to install it, and when to use it.
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Deep Dives·3 min
Getting Started with RAG: A Complete Guide from LLM Hallucinations to Retrieval-Augmented Generation
A deep dive into RAG (Retrieval-Augmented Generation) technology, covering LLM hallucinations, data staleness, and limited expertise, plus RAG workflows, core components, and LangChain learning paths.
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Tutorials·3 min
Python Learning Path: A Three-Stage Systematic Approach from Zero to Practical Projects
A detailed guide to Python's three learning stages: syntax fundamentals, advanced concepts, and practical applications. Covers OOP, web scraping, data analysis, and automation with timeline planning.
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Tutorials·3 min
Efficient PyTorch Learning: A Source Code-Driven Methodology
A proven PyTorch learning method: spend 2-3 days on basics, then advance rapidly by reading U-Net and ViT source code line by line. Master PyTorch through source code-driven learning.
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Tutorials·3 min
The Complete Vibe Coding Workflow: A Step-by-Step Guide for Non-Developers to Build and Ship Products Independently
A complete 7-step Vibe Coding guide from requirements to deployment. Learn how non-developers can independently build and ship products using VS Code and Claude Code, with PRD writing, static Demo validation, and step-by-step development.
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Tutorials·3 min
LLM Learning Roadmap: A Complete Guide from Beginner to Project Implementation Across Seven Core Modules
A systematic breakdown of seven core LLM learning modules covering environment setup, Prompt Engineering, RAG, Agents, dev frameworks, fine-tuning, and hands-on projects for developers.
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Tutorials·3 min
PyTorch Beginner Tutorial: A Complete Guide to Tensor Operations and Neural Network Construction
A detailed PyTorch beginner guide covering tensor operations, dynamic computational graphs, GPU acceleration, and building your first neural network with nn.Module, with learning path recommendations and code examples.
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