#LLM application developer interview
170 related articles
·7 min
Getting Started with Dify: A Complete Guide from Local Deployment to Building Enterprise-Grade AI Applications
A detailed guide to Dify, the open-source LLM app development platform, covering its core features and full local deployment via VMware + Ubuntu + aaPanel + Docker. Supports 100+ models like DeepSeek and ChatGPT to build enterprise AI apps fast.
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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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·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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·3 min
Building LangGraph from Scratch in TypeScript: A Complete Agent Development Guide for Frontend Engineers
How can frontend developers get into AI Agent development with TypeScript? This guide covers a four-stage path from API calls to building LangGraph from scratch, including Zod, state management, and node-edge design.
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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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·6 min
video-use: The Open-Source Tool That Lets AI Coding Agents Auto-Edit Videos
video-use from the browser-use team lets AI coding Agents auto-edit videos via natural language. 13K+ GitHub stars, batch processing, silence removal, FFmpeg integration.
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·2 min
Building an AI Public Opinion Monitoring Platform with 7 Agents: A Full-Stack LangGraph Deep Dive
A deep-dive into an AI public opinion monitoring platform built with LangGraph and LangChain, featuring 7 collaborative agents, ES vector search, email alerts, and automated report generation.
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·4 min
Building an AI Agent Framework from Scratch: Deep Dive into Tool Calling, Memory, and Dual-Model Scheduling
Build an AI Agent from scratch — no frameworks. Deep dive into Function Call schema design, MCP remote mirroring, dual-model routing, and short-term memory management.
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·4 min
Frontend to Full-Stack AI: A Complete NestJS + LangChain Roadmap
Pure frontend roles are shrinking fast. Learn how mastering NestJS and LangChain AI agent development can unlock a 20–30% salary boost on your full-stack AI transition path.
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·3 min
Claude Code in Practice: Writing Maintainable AI Code with the Harness Methodology
Learn how to write controllable, maintainable AI code using the Harness methodology with Claude Code. Covers SDD, Agent orchestration, and enterprise-grade AI programming practices.
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·3 min
Harness Architecture Explained: The Next-Generation Design Paradigm for Agent Development
A deep dive into Harness Architecture — the next-gen Agent design paradigm. Covers its evolution from prompt engineering and context engineering, multi-agent collaboration, sandbox security, feedback loops, and why it's a must-have for LLM developer interviews.
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·3 min
Benchmarking Popular Claude Skill Libraries: A 5-Step Core Pattern for AI Agent Development
A deep-dive evaluation of Addy Osmani, Matt Pocock, and Gary Tan's skill libraries, distilling a 5-step Research→Prototype→Plan→Build→Test agent dev loop and why the best skill system is always your own.
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·4 min
Adding a 'Repair Layer' to Open-Source Models: How DeepSeek Can Outperform Claude Opus
Independent developer Ahmad Awais found that open-source LLM failures stem from Tool Calling bugs, not model capability. A deterministic repair layer + repair hints can make DeepSeek outperform Claude Opus.
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·3 min
Building a RAG Q&A Workflow with Dify: A 4-Step Guide to Enterprise Knowledge Base Chatbots
Learn RAG fundamentals and build an enterprise knowledge base chatbot with Dify in 4 steps: data prep, model config, knowledge base import, and workflow orchestration.
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·6 min
OpenAI CFO Deep Dive: IPO Stance, Compute Shortage, and Jony Ive's New Hardware Revealed
OpenAI CFO Sarah Fryer discusses the $122B fundraise, IPO timeline, Anthropic rivalry, compute shortage crisis, and the mysterious Jony Ive hardware collaboration on the All-In Podcast.
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·7 min
AI Agent Development: A Four-Stage Competency Model from 15K to 40K RMB Salary Growth Path
A detailed four-stage competency model for AI Agent development: from Python/RAG basics (15K) to workflow orchestration (20K), inference optimization (30K), and Agent cluster governance (40K RMB).
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·2 min
Harness Engineering Architecture Explained: An Essential Skill for AI Agent Developer Roles
Harness Engineering is becoming a must-have skill for AI agent developer roles. Learn the architecture, how top agent products use it, and how to practice with LangChain DeepAgents.
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