#SQL for data interviews
51 related articles
·5 min
Java Backend Interview Sprint: A Systematic Methodology From Rote Q&A to Scenario Questions
How can you prepare efficiently for a Java backend interview? This article breaks down the core methodology of "process-driven interview engineering," covering resume optimization, understanding principles, scenario analysis frameworks, and production troubleshooting.
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·4 min
The Sprout Experiment: Exploring Deterministic Symbolic Reasoning AI Without Neural Networks
Sprout is a contrarian AI research experiment that abandons GPUs and neural networks in favor of deterministic symbolic reasoning. It features an auditable knowledge base and refuses to answer when evidence is insufficient, prioritizing explainability and governance.
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·6 min
Vibe Coding in Practice at Big Tech: How Frontend Engineers Can Avoid Being Replaced in the AI Era
A big-tech interviewer reveals: junior/mid frontend dev is being replaced by AI. This article breaks down 3 core Vibe Coding interview questions to help you master key skills for the AI-assisted coding era.
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·4 min
Spring AI Alibaba Graph in Practice: HR Recruitment Workflow Agent Design and Core Technical Analysis
Build an HR recruitment workflow Agent with Spring AI Alibaba Graph, covering resume parsing, job matching, tiered question generation, HITL checkpointing, and time travel state rollback across 20 core technical points.
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·6 min
Skill vs MCP: The Essential Difference Between Two Types of AI Agent Capabilities and How They Work Together
Skill and MCP are two core concepts for building AI Agents. Skill encapsulates task execution methodology, while MCP provides a standardized protocol for connecting external tools. This article breaks down their core differences, abstraction levels, and collaboration.
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·7 min
6 Core Standards of a High-Value Agent Project That Impress Interviewers
Most Agent projects lack competitiveness in interviews due to missing business value and engineering depth. This article breaks down the 6 core standards of high-value Agent projects.
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·6 min
LangChain 1.3 in Practice: A Complete Guide to DeepAgent Architecture and the Harness Philosophy
An in-depth look at LangChain 1.3's core modules and DeepAgent architecture—covering the Harness philosophy, LangGraph internals, HITL, memory management, and guardrails to master production-grade AI Agent development.
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·7 min
LangChain.js + Nuxt.js: An AI Full-Stack Advancement Path for Frontend Engineers
Frontend hiring now treats AI capabilities as a core assessment, covering RAG knowledge bases, AI Agent development, and LangChain.js engineering. Learn how LangChain.js + Nuxt.js helps frontend developers build memory- and retrieval-capable AI full-stack apps.
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·7 min
Building AI Agent Frameworks with TypeScript: The Core Development Paradigm for Full-Stack Developers
An in-depth look at why TypeScript is the top choice for AI Agent development: covering Zod structured output validation, LangGraph's graph state machine design, and a full learning path for front-end devs transitioning to full-stack AI.
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·3 min
The Interview Game in the AI Era: Where Does Human Expertise Still Matter?
From CSS-trap interview questions to 30K-line code patches, explore where human expertise truly matters in the Agent Coding era — and why AI raises the bar, not lowers it.
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·3 min
Harness Engineering in Practice: A Guide to Enterprise-Grade Multi-Agent System Architecture
A deep dive into Harness Engineering architecture: building an AI procurement assistant on ERP systems, covering multi-agent orchestration, MCP protocol, ASGI deployment, and sandbox isolation.
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·4 min
LLM Job Interviews: Multi-Agent and Harness Engineering Are Now Must-Know Topics
Deep analysis of LLM job interview essentials: Multi-Agent architecture, Harness engineering, Agent Loop, sandbox isolation, and memory management with career transition tips.
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·2 min
Career Switch to AI Software Testing from Scratch: A Complete 3-Month Learning Roadmap
A complete 3-month learning roadmap for switching to AI software testing from scratch, covering fundamentals, Python automation, AI-powered testing, and career guidance.
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·3 min
Deep Dive into Claude Sonnet 4: Replicating Lovable with Just Two Prompts
Deep dive into Claude Sonnet 4: replicate Lovable with two prompts, generate McKinsey-grade reports, build 2D games, and explore the AI Agent building block economy.
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·2 min
Java Backend Interview Prep Roadmap: A 6-Week Sprint Guide Integrating AI Agent Development
A systematic 6-week Java backend interview prep roadmap covering JVM internals, Spring Boot, Redis, microservices, plus Spring AI, LangChain4j, and RAG for AI Agent development.
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·3 min
Claude Code Enterprise E-commerce in Practice: A Methodology for Taking AI Programming from Demo to Industrial-Grade
A deep dive into engineering methodology for enterprise e-commerce development with Claude Code and Harness AI, covering architecture, code quality, and CI/CD practices.
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Tutorials·4 min
Skills vs Coze vs Dify: A Hands-On Comparison — Which AI Tool Is Best for Auto-Generating Test Cases?
Hands-on comparison of Skills, Coze, and Dify for AI-powered software testing: auto-generating test cases from PRDs, performance reports, and requirements Q&A bots.
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Tutorials·4 min
Skills vs Coze vs Dify: A Hands-On Comparison — Which AI Tool Is Best for Auto-Generating Test Cases?
Hands-on comparison of Skills, Coze, and Dify for AI-powered software testing — covering auto-generated test cases from PRDs, performance reports, and Q&A bots to help testers choose the right tool.
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Industry Insights·3 min
Why Does DeepSeek Dare to Go Open Source? A Deep Dive into the Business Logic and Strategic Thinking
Deep analysis of DeepSeek's open-source strategy: why it's the optimal survival play. From user growth and tech moats to national strategy, decoding Liang Wenfeng's vision.
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Tutorials·3 min
Frontend AI Full-Stack Development in Practice: Building Multimodal Applications with PNPM MonoRepo Architecture
A practical guide to frontend AI full-stack development covering PNPM MonoRepo architecture, TurboRepo build optimization, and LangChain multimodal applications with Ollama local model deployment.
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