#positional encoding
200 related articles
·3 min
AI Agent Core Architecture Breakdown: From Concept to Enterprise-Grade Intelligent Agent Development
Deep dive into AI Agent architecture: perception, brain, and action modules. Covers RAG memory systems, tool calling mechanisms, Chain of Thought reasoning, and enterprise agent development roadmap.
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·4 min
Complete AI Large Language Model Learning Roadmap: From Beginner to Production-Ready
A systematic guide to learning AI large language models, covering Transformer architecture, prompt engineering, RAG, AI Agents, fine-tuning, and enterprise projects from beginner to production-ready.
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·3 min
Minimax M3 vs DeepSeek V4 Hands-On Test: Who Builds a Better Dino Run Game?
Hands-on comparison of Minimax M3 and DeepSeek V4 Pro building a Dino Run game from the same prompt, revealing how native multimodal AI changes game dev.
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·3 min
Xiaomi Open-Sources MiMo Code: Can an Infinite Memory Mechanism Solve AI Coding's Biggest Pain Point?
Xiaomi open-sources MiMo Code, an AI coding tool with infinite memory, multi-Agent collaboration, and Claude Code compatibility — solving context forgetting in large projects.
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·3 min
Using AI Tools to Write Your Graduate Thesis: Completing Deep Learning Experiments Without a CS Background
How can non-CS graduate students use AI tools like Cursor to efficiently complete their thesis? A complete guide covering data sourcing, code adaptation, and AI-assisted modifications.
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·2 min
Claude Fable User Guide: Four Core Tips to Boost AI Development Efficiency
Anthropic shares four key tips for Claude Fable: assign bigger tasks, choose effort levels wisely, rewrite old instructions, and shift from tasks to goals.
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·4 min
Unity MCP Hands-On Guide: Lessons Learned from AI-Powered Game Engine Operations
In-depth hands-on guide to Unity MCP setup and usage, covering environment config, run_command best practices, Prefab sandbox workflows, and a comparison of Copilot, Claude Code, Codex, and DeepSeek.
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·4 min
Core Methodology of Prompt Engineering: A Systematic Deep Dive from Principles to Practice
A deep dive into prompt engineering principles and core methodology. Master three keys to high-quality prompts: specific, rich, and unambiguous. Learn tuning techniques and advanced programming integration.
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Deep Dives·1 min
Demystifying Transformer: A Word-Continuation Function, Deconstructed
Understand Transformer through the lens of word continuation. Breaking down language generation into Embedding, Transformer Block, and Probability output modules for intuitive understanding.
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Deep Dives·4 min
Is Context Engineering the Core of Agent Development? A Deep Dive into Architecture and Practical Solutions
Deep dive into context engineering as the core of Agent development, covering five context modules, four pain points, and dynamic assembly solutions including compression, hybrid retrieval, multi-Agent architecture, and state machine control.
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Tutorials·3 min
Claude Code Hooks Explained: A Safety Net for When Rules Fail
Deep dive into Claude Code Hooks: why CLAUDE.md rules fail, and how PreCommand blocking, PostCommand reminders, and Stop Hooks form a reliable AI behavior constraint system.
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Tutorials·3 min
Claude Code Hooks Explained: A Safety Net for When Rules Fail
Deep dive into Claude Code Hooks: why CLAUDE.md rules fail and how PreCommand, PostCommand, and Stop Hooks provide a reliable safety net through on-demand context injection.
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Tutorials·1 min
AI Agent Learning Roadmap: A Complete Guide from LLM Fundamentals to Enterprise-Level Project Implementation
A systematic AI Agent learning roadmap covering Python setup, Prompt Engineering, RAG, LangChain, multi-Agent collaboration, with enterprise medical consultation system case study and phased learning plan.
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Tutorials·1 min
AI Large Language Model Learning Roadmap: Six Stages from Zero to Engineer
A systematic LLM engineer learning roadmap covering Transformer basics, prompt engineering, RAG, Agent development, API integration, fine-tuning, deployment, and project practice across six stages.
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Deep Dives·4 min
Core Principles of the Transformer Architecture: A Deep Dive into Self-Attention Mechanisms and Engineering Optimizations
Deep dive into Transformer architecture covering self-attention QKV mechanics, Encoder-Decoder structure, Flash Attention memory optimization, RoPE positional encoding, and GQA inference acceleration.
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Tech Frontiers·2 min
Step 3.7 Flash: Deep Dive into the 198B Sparse MoE Multimodal Model
Deep dive into StepFun AI's Step 3.7 Flash, a 198B sparse MoE vision-language model with 256K context and 3-level reasoning, excelling in multimodal understanding, AI coding, and Agent tool orchestration.
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Tutorials·3 min
Complete Guide to Connecting Claude Code with DeepSeek-V4
Complete guide on connecting DeepSeek-V4 to Claude Code, covering Node.js installation, environment variable configuration, model mapping, and real-world coding tests for a near-premium AI programming experience with open-source models.
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Tutorials·3 min
Getting Started with AI Full-Stack Development: A Knowledge Framework from Machine Learning to Large Language Models
A systematic guide to the relationships between AI, machine learning, deep learning, and large language models, helping developers build a clear knowledge framework and find an efficient learning path.
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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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Product Reviews·2 min
GPT 5.4 vs Opus 4.7 vs Kimi K2.6 Code: Real-World Programming Comparison
Real-world comparison of GPT 5.4, Claude Opus 4.7, and Kimi K2.6 Code across backend, frontend, cost-effectiveness, and tooling to help developers choose the best AI coding assistant.
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