#software engineering interviews
135 related articles
·4 min
Why General-Purpose Agents Don't Work: How a Job Interview Reshaped One Developer's Thinking
An Agent developer's three-round interview reveals why general-purpose Agents are a dead end for startups. The path forward: vertical Agents, domain context, and iteration speed as a moat.
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·6 min
A Three-Month AI Agent Learning Roadmap: From Zero to Hands-On Agent Development
A systematic breakdown of the complete AI Agent learning roadmap, covering prompt engineering, the ReAct paradigm, memory mechanisms, and multi-agent collaboration, with hands-on project advice.
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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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·4 min
Career Switch to LLM Development: A Four-Level Skill Progression Roadmap
Want to switch careers into LLM development but don't know where to start? This guide breaks down a four-level skill roadmap — from basics and API calls to RAG, fine-tuning, Agent development, and multimodal — to help you build real AI career value.
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·5 min
OSWorld 2.0 Benchmark: Top AI Agents Achieve Only 20% Completion Rate — Long-Horizon Tasks Are the Fatal Weakness
OSWorld 2.0 benchmark tests 108 long-horizon computer tasks (median 1.6 hrs for humans). Claude Opus tops out at 20.6% completion, exposing critical AI Agent weaknesses in state maintenance and self-correction.
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·5 min
OSWorld 2.0 Benchmark: Top AI Agent Achieves Only 20% Completion Rate — Long-Horizon Tasks Are the Achilles' Heel
OSWorld 2.0 benchmark tests 108 long-horizon computer tasks. Claude Opus tops at only 20.6% completion, exposing critical AI weaknesses in state tracking and error self-correction.
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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
TypeScript + Zod: Dual Insurance for Type Safety in AI Agent Development
An in-depth look at the division of labor between TypeScript and Zod in AI Agent development: TypeScript handles compile-time static type checking, Zod handles runtime validation, forming a dual defense.
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·6 min
From Frontend Engineer to AI Agent Developer: A Complete High-Paying Skill Tree Breakdown
Pure frontend roles are shrinking; AI Agent development is the high-salary divide. This guide breaks down the full skill tree for frontend engineers pivoting to AI: TypeScript, frameworks, AI productivity, and Agent core concepts (MCP, Tool Calling, Skill).
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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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The True Nature of Moving Fast: Freque…
·3 min
The True Nature of Moving Fast: Frequent Contact with Reality Is a Form of Humility
"Moving fast" isn't recklessness — it's intellectual humility. This article explores the shared logic behind Agile, MVP, and AI iteration: true arrogance is believing you don't need to be tested.
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·4 min
Getting Started with Software Testing: A Complete Guide to Core Concepts and Classification Systems
A comprehensive guide to software testing fundamentals covering definitions, purposes, classification by phase, technique, and method, plus core concepts like smoke testing and regression testing.
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·3 min
Agent + Skill Architecture: A Practical Guide to AI-Driven End-to-End Automated Testing
Learn how to build an AI-driven API automation testing framework using Agent+Skill architecture with Claude Code, covering test case generation, script execution, and report output.
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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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·4 min
Feishu-Style Docs + AI Knowledge Base in Practice: A Complete Guide to RAG and TipTap Editor Development
Learn how to build a Feishu-style document system with TipTap editor, integrating AI auto-completion, document continuation, and RAG knowledge base Q&A with vector databases and Embedding.
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·4 min
Building a Coding Agent from Scratch: Creating a Codex-like AI Programming Tool
Deep dive into AI coding agent architecture: from interview-level cognition to building a Codex-like CLI agent tool, covering agents.md, Skills systems, context management, and more.
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·3 min
Agent Skills Architecture Explained: A New Paradigm for Modular AI Agent Development
A deep dive into Agent Skills architecture: core concepts, components, and how it works. Clarifies common misconceptions about Skills vs. MCP, and compares Skills with Multi-Agent architecture.
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·3 min
Claude Code Debugging Guide: From Context Management to the Observability Flywheel
A systematic guide to Claude Code debugging and observability, covering Token monitoring, context management, Compact compression, security, and Skills ecosystem.
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