#AI learning path
2888 related articles
Tutorials·3 min
Getting Started with AI Agent Development: A Complete Learning Path from Concepts to Practice
A comprehensive guide to AI Agent development for beginners, covering core concepts, market outlook, LangChain framework, RAG knowledge bases, and hands-on projects to systematically master intelligent agent development skills.
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Tutorials·2 min
LangChain 1.0 Complete Guide: Architecture, Core Modules, and Learning Path
Deep dive into LangChain 1.0's three-layer architecture (LangChain, LangGraph, Deep Agents), core components like Models, Tools, and Memory, plus a complete learning path from semantic search to multi-agent collaboration.
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Tutorials·3 min
Java Full-Stack Beginner's Guide: Application Domains, Three Major Platforms & Learning Path Explained
A beginner's Java full-stack guide covering Java's origins, real market position, enterprise server-side strengths, Java SE/EE/ME platforms, and a complete learning path from basics to full-stack.
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Tutorials·3 min
The Complete Guide to Spring AI: A Full Learning Path for Java Engineers Building AI Applications
A comprehensive guide to Spring AI covering LLM integration, prompt engineering, RAG knowledge bases, and five AI Agent patterns, with three enterprise projects for Java engineers.
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Tutorials·2 min
Learning AI Large Language Models from Scratch: A Guide to Learning Paths, Hardware, and Programming Languages
A beginner's guide to learning AI large language models — covering learning paths, hardware requirements, Python essentials, and cloud services for learners at every level.
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Tech Frontiers·3 min
AI Test Development Learning Path: A Practical Guide from Prompt Engineering to RAG Knowledge Bases
A systematic AI test development learning path covering LLM fundamentals, prompt engineering, PyTest automation, RAG knowledge bases, and MCP tool chains to help QA engineers master AI-empowered testing.
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Tutorials·1 min
LangChain Agent Development Tutorial: RAG and Agent Hands-On Learning Path
A systematic learning path for LangChain Agent development covering RAG, autonomous Agent decision-making, and tool calling—from zero to production-ready projects.
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Tutorials·3 min
2026 AI Learning Roadmap: 3 Proven Paths + A 4-Step Action Checklist (With Complete Technical Framework)
How to learn AI in 2026 without wasting time? This guide covers 3 proven paths — AI Agent customization, vertical specialization, and full-stack LLM development — plus a 4-step action checklist.
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Tutorials·3 min
Programmer Yupi's AI Resource Collection: A Complete Learning Path from Zero to Production
A deep dive into the popular GitHub project liyupi/ai-guide covering Vibe Coding tutorials, LLM usage, AI coding tools (Cursor/Claude Code), Prompts, RAG/MCP/Agent concepts — a 13,000+ Stars Chinese AI learning resource hub.
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·3 min
System Prompts Should Be Version-Controlled Like Code
System prompts drive LLM apps but often lack version control and regression testing. Learn how to manage them with versioning, structured separation, testing, and code review.
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·3 min
Getting Started with MARL (Multi-Agent Reinforcement Learning): A Complete Path from Theory to Code
A systematic guide to learning MARL from theory to code, covering CleanRL, PettingZoo, PyMARL tools, IQL/VDN/QMIX/MADDPG algorithm progression, and practical tips for bridging theory and implementation.
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·3 min
Guide to Acquiring Stereo Camera + IMU Fusion Datasets: Open-Source Resources and Combination Strategies
A detailed guide on acquiring large-scale stereo camera and IMU synchronized datasets, covering KITTI, EuRoC, nuScenes, Waymo, and strategies for combining datasets while avoiding synchronization pitfalls.
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·3 min
The Return of Private Cloud: A Rational Choice in the Era of Data Sovereignty and Compute Autonomy
After a decade of public cloud dominance, private cloud is making a comeback. From cost recalculation and data sovereignty compliance to AI compute autonomy, we analyze why enterprises are reconsidering private and hybrid cloud strategies.
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·3 min
Ten Breakthroughs in Mathematics and Theoretical Computer Science and Their Far-Reaching Impact
An in-depth look at ten major advances in mathematics and theoretical computer science, covering complexity theory, combinatorics, and derandomization, and how they impact cryptography, AI training, and quantum computing.
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·2 min
General Agents Are Essentially Coding Agents: The Paradigm Shift from Tool Calling to Code Execution
Explore why general AI agents are essentially coding agents. From Turing completeness to composability and verifiability, discover the paradigm shift from Function Calling to Code as Action.
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·3 min
Flint: A Deep Dive into the Visualization Language Optimized for AI Generation
Deep analysis of the Flint visualization language design philosophy, exploring how its declarative syntax and structured Schema optimize for LLM generation, enabling AI to efficiently create charts.
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·3 min
Kimi-K3 Scores 60.4% on ARC-AGI-2: A Breakthrough in Abstract Reasoning
Kimi-K3 scores 60.4% on ARC-AGI-2, far surpassing most LLMs. This article analyzes what ARC-AGI-2 tests, what this score means for abstract reasoning, and its implications for the AI industry.
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·6 min
Using RL to Please the Reward Model: The Reward Hacking Concern Behind Soaring Elo Scores
When RL continuously optimizes models to please reward models, do soaring Elo scores truly represent capability gains? A deep dive into Reward Hacking in RLHF, Goodhart's Law in AI, and industry countermeasures.
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·3 min
Reasoning Capabilities Unmatched, Pricing Still Too High: The Dilemma of AI Model Competition
Deep analysis of the dilemma in AI model competition where reasoning gaps and pricing imbalances force vendors to excel at either capability or cost-effectiveness to survive.
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·1 min
The Third Dimension of Pretraining: A New Paradigm Claiming 6.2x Sample Efficiency and 250x Inference Speedup
Harvard and UIUC propose a third axis of pretraining, claiming 6.2x sample efficiency and 250x inference speedup. Deep analysis of this new paradigm's implications and key caveats.
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