#fine-tuning
171 related articles
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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Tutorials·3 min
Complete Learning Roadmap for Java Developers Transitioning to AI Engineering
A detailed five-phase learning roadmap for Java developers transitioning to AI engineering, covering Spring AI, LangChain4j, RAG core technology, and Agent development.
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Expert Opinions·3 min
The Salary Ceiling for Agent Engineers: Two Critical Dividing Lines
Agent engineer salary gaps hinge on two dividing lines: real production deployment experience and depth of foundational theory including deep learning, fine-tuning, and reinforcement learning.
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Deep Dives·4 min
AI Agents 101: Core Concepts, Technical Architecture, and How They Work
A beginner's guide to AI Agents: understand core concepts, the perception-decision-action loop, LLM, tool calling, memory systems, and RAG architecture explained from scratch.
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Deep Dives·3 min
DeepSeek V4 Deep Technical Breakdown: Million-Token Context and Extreme Cost Efficiency
Deep analysis of DeepSeek V4's core architecture: Hybrid Compressed Attention, Manifold-Constrained Hyperconnection, and MUON optimizer—how they cut inference costs by 10x and enable million-token context processing.
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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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Tutorials·4 min
Complete Guide to Building a Local AI Knowledge Base with Qwen3.5 + RAGFlow + Ollama
Complete guide to building a local AI knowledge base with Qwen3.5, RAGFlow, and Ollama, covering Docker deployment, Embedding model configuration, knowledge base creation, and RAG system setup.
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Tutorials·3 min
Stable Diffusion Local Deployment Guide: Run AI Image Generation Free with 8GB RAM
Complete guide to deploying Stable Diffusion locally. Covers hardware requirements, one-click installation, and model setup. Run AI image generation free with 8GB RAM.
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Tutorials
The Complete Guide to Using O3 Mini fo…
·3 min
The Complete Guide to Using O3 Mini for Free: 5 Zero-Cost Solutions
Discover 5 free ways to use OpenAI's O3 Mini model, including AnyChat unlimited access, GitHub Models API, and Aider clipboard mode for zero-cost AI experience.
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Tutorials·2 min
How Enterprise Text-to-SQL Breaks the 90% Accuracy Barrier: A Practical Guide to Agent Architecture
A deep dive into engineering strategies for enterprise Text-to-SQL to break 90% accuracy, covering precise schema retrieval, multi-Agent architecture, self-correction, and AI coding practices.
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Tech Frontiers
OpenAI Codex Deep Dive: AI Programming…
·3 min
OpenAI Codex Deep Dive: AI Programming Evolves from Code Completion to Full-Repository Autonomous Development
Deep analysis of OpenAI Codex agent and its Codex-1 model, comparing Windsurf's SW1E1 and Anthropic's new reasoning model, exploring how AI programming leaps from single-file assistance to full-repository autonomous understanding.
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Industry Insights·3 min
OpenAI Models Officially Land on AWS Bedrock, Ushering in a New Multi-Cloud Era for Enterprise AI Deployment
OpenAI's frontier models and Codex are now GA on Amazon Bedrock, letting enterprises leverage AWS security and compliance to access OpenAI capabilities. A deep dive into the multi-cloud AI impact.
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Industry Insights·2 min
Two Major Career Paths in AI Large Language Models: Engineering Implementation vs. Algorithm Research
In-depth analysis of two core AI LLM career paths: engineering implementation vs. algorithm research. Compare education requirements, skills, and job prospects for programmers transitioning to AI.
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Tutorials·3 min
Can You Really Transition to AI/LLM in Three Months? A Deep Dive into the Learning Roadmap
Deep dive into a popular 3-month AI/LLM transition roadmap: from Python basics and Prompt engineering to LangChain, RAG, Agents, and hands-on projects, with realistic time estimates and pitfall warnings.
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Tutorials·3 min
Hermes Agent in Practice: A Complete Breakdown from ReAct Loop to Autonomous Skill Evolution
Deep dive into Hermes Agent's four progressive cases: terminal ReAct loop, Feishu AI assistant, four-layer persistent memory, and three-stage Skill evolution with DeepSeek support.
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Tutorials
Vibe Coding in Practice: From Idea to …
·4 min
Vibe Coding in Practice: From Idea to Deployment in Just Five Steps
A detailed guide to the complete Vibe Coding workflow: design UI with Google Stitch, generate frontend with AI Studio, build backend with AntiGravity—go from idea to deployed app in five steps with zero coding experience.
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Product Reviews
Comprehensive Review of 10 Mainstream …
·4 min
Comprehensive Review of 10 Mainstream AI Coding Tools: How to Choose from Cursor to Claude Code
In-depth comparison of 10 AI coding tools including GitHub Copilot, Cursor, Claude Code, and Windsurf, analyzed across features, target users, and pricing to help developers choose the right AI assistant.
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Product Reviews
Cursor Composer 2.5 Hands-On: An AI Co…
·2 min
Cursor Composer 2.5 Hands-On: An AI Coding Model That's Faster and 10x Cheaper
Hands-on review of Cursor Composer 2.5's Agent view, Plan mode, and right panel features. Coding ability matches Claude and GPT top models at up to 10x lower cost with significantly faster speed.
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Tech Frontiers·2 min
Claude Opus 4.8 Thinking Effort Calibration Explained: A Critical Optimization Direction for AI Reasoning Models
Anthropic releases Claude Opus 4.8 with optimized thinking effort calibration. This article explains what it is, why it matters for AI reasoning models, and its impact on industry competition.
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