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JavaBetter: Java Advanced Learning Path

website·Indexed 32 days ago

https://tobebetterjavaer.com/

Comprehensive Java learning resources covering core fundamentals, JVM, and concurrency. Includes specialized content on AI Agent interview questions, AI programming practicals using Claude Code and DeepSeek, structured learning roadmaps, and technical guides on advanced AI architectures.

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41
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15%

What's inside JavaBetter

  1. Understand the difference between MiMo-V3 HySparse2 and Hybrid Sliding Window Attention

    MiMo-V3's HySparse2 differs from standard hybrid sliding window attention by combining Self-Decoder, Cross-Decoder, KV Reuse, and KV Bridging. This architecture allows the model to either reuse KV Cache or compute it from the first half of the sequence for the second half, effectively reducing the Prefill computation overhead for long input sequences.

  2. Understand AI Agent architecture and core components

    An AI Agent is more than a single LLM API call; it is a system composed of a model, tools, memory, and a runtime (Harness). Unlike a ChatBot, an Agent possesses tool-calling capabilities, task planning, and memory, allowing it to observe an environment, execute actions, and make autonomous decisions based on results.

    Core components of a production-grade Agent include:

    • Model: The reasoning engine.
    • Tools: External functions the agent can invoke.
    • Memory: Storage for short-term state and long-term knowledge.
    • Planning: The ability to break down goals into steps.
    • Harness: The runtime environment responsible for the execution loop, permissions, context management, and error recovery.

  3. Understand Claude Code Core Concepts and Architecture

    For developers using or building AI coding agents, the following core concepts and architectural patterns from Claude Code are highlighted:

    • Core Concepts: Context management, CLAUDE.md configuration, Hooks, Skills, and Subagents.
    • Architecture: 6 core abstractions, 7-level permission modes, and the use of async generators for Query Loops.
    • Design Patterns: Recursive Agent architecture and specific design patterns for Agent Harnesses.

  4. Study Java core fundamentals and advanced topics

    The Java Core Tutorial Overview provides a structured directory for learning Java, starting from basic syntax and moving through collection frameworks, concurrent programming, and the Java Virtual Machine (JVM).

  5. Access Java learning resources on javabetter.cn

    javabetter.cn provides a comprehensive set of Java learning materials created by 沉默王二. The site covers a spectrum from basic Java syntax and collection frameworks to advanced topics such as concurrent programming and JVM internals. It also includes practical guides on AI programming tools (Claude Code, Codex, Agent, Skills) and curated interview question banks.

  6. Prepare for AI Agent Engineering Interviews

    The site provides comprehensive interview question banks for AI Agent roles across various top-tier companies. Key technical topics covered include:

    • Memory Systems: Three-layer memory architecture, Agent Memory vs. DRAM, and state persistence/fault recovery.
    • Context Management: Context compression, KV Cache expansion principles, and long-text handling.
    • Agent Architecture: Single-Agent vs. Multi-Agent orchestration, Plan design, and SubAgent coordination.
    • Tooling & Skills: Tool vs. Skill distinctions, MCP (Model Context Protocol), function calling, and progressive skill disclosure.
    • Evaluation & Quality: Agent evaluation harnesses, badcase analysis, hallucination control, and safety guardrails.
    • LLM Fundamentals: Transformer, Attention, MoE, RLHF, SFT, and GRPO.

  7. Evaluate and Use AI Coding Agents and Tools

    The documentation provides practical benchmarks and setup guides for several AI coding tools:

    • MiniMax Code CLI: Analysis of the v0.4.12 open-source architecture, focusing on Turn Loop, tool systems, and extension registration.
    • Kimi Code: Performance comparisons between K2.7 and K3 versions for complex UI/animation generation.
    • DeepSeek V4 Series: Evaluations of V4.1 Flash and V4 Pro, including the use of the Responses API and performance benchmarks using Kimi Code as a harness.
    • Codex + Jev: Testing the integration of Jev in Codex, noting that Agent decision-making can be 40-200x faster and 40-400x cheaper than standard LLM calls with free output tokens.

  8. Migrate from MinIO to RustFS for RAG Projects

    Practical implementation guide for replacing MinIO Community Edition with RustFS. The workflow includes:

    1. Product evaluation.
    2. Docker deployment.
    3. Spring Boot integration.
    4. Data migration process.

  9. Secure an Agent Sandbox

    Command blacklists are insufficient for dynamically generated code. A robust sandbox must provide isolation across four layers:

    1. File System
    2. Network
    3. Process Space
    4. Resource Quotas

  10. Handle PDF documents in Agent RAG

    PDF RAG requires simultaneous processing of layouts, paragraphs, tables, images, and reference positions. The recommended approach involves structured parsing, chunk-based retrieval, and multimodal supplementation.

  11. Find learning roadmaps for various programming languages and technologies

    Detailed learning roadmaps and resources are available for the following technologies:

    • Languages: Go, C, C++, Python, .NET
    • Infrastructure & OS: Linux, Operating Systems
    • Databases: MySQL, Redis
    • Specialized Fields: Data Structures & Algorithms, Android, Big Data, Front-end development, and Lanqiao Cup (competition prep).

  12. Design Multi-Agent collaboration and conflict resolution

    Multi-agent systems require defined roles, task distribution, context isolation, and result aggregation.

    Conflict Resolution Strategies:

    • Hierarchical: Disagreements between a Main Agent and Sub-agents are resolved by the Main Agent's decision and context isolation.
    • Deterministic: Conflicts between a Worker and a Reviewer are resolved using deterministic environments (e.g., running unit tests).
    • Concurrency: Write conflicts between concurrent workers are prevented using worktrees, file-based division of labor, and optimistic locking with version numbers.