128 related articles

Resume full of RAG and Agent but keep failing interviews? The issue is you only run demos and can't explain production engineering challenges. This article breaks down data cleaning, hybrid retrieval, hallucination protection, and agent loop breakers.

A systematic guide to the full DeepSeek Agent development process: covering prompt engineering, the ReAct framework, workflow orchestration, local deployment, and business requirement breakdown for commercial-ready AI Agents.

An AI/ML engineer with 4 years of experience faced 10 failed interviews in 15 months at Meta, Amazon, and more. This deep analysis breaks down the root causes and offers ML interview strategies and mindset advice.

No Amazon on-campus recruiting? This guide details the off-campus path for CS students: DSA practice strategy, ML/LLM skill-building, portfolio creation, resume optimization, and referral tips.

A collection of 28 fully reproducible enterprise-grade AI Agent projects covering code debugging, financial analysis, customer service, and multi-agent collaboration—deployable even for beginners.

Prompt engineering and RAG are just the basics. Real enterprise AI runs on Agents. Explore the 4 stages of LLM deployment, Agent core capabilities, and industry trends.

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.

In-depth guide to Kaggle's free-tier compute: P100/T4 GPU with 30 hours/week quota, 12-hour sessions, suitable models like CNN and BERT fine-tuning, plus tips like mixed precision and checkpointing to start deep learning at zero cost.

Learn automation testing from scratch! This article breaks down a three-stage path: Selenium/Appium tools, Requests+PyTest API testing, performance testing and CI/CD, with real projects—build a complete skill set in 21 days.

A systematic four-stage roadmap for AI Agent development: fundamentals, core principles, enhancement, and real-world deployment. Build complete Agent skills.

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.

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.

An in-depth analysis of the EU's Chat Control legislative proposals: from 1.0 voluntary scanning to 2.0 mandatory detection orders, revealing the threat of client-side scanning to end-to-end encryption and the privacy vs. child protection debate.

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.

Shellular lets developers remotely control AI coding assistants like Claude Code and Codex from their phones. A deep dive into the problem it solves, its architecture, and the real demand for mobile AI coding.

Unsloth v0.1.45-beta adds Gemma 4 MTP support, AMD ROCm & NVIDIA Blackwell fixes, a new Hub download manager, and a compact RAG system for local LLM fine-tuning.

An in-depth analysis of LangGraph's core concepts: short-term and long-term storage mechanisms, its differences from LangChain, the MIT open-source license, and private deployment solutions for enterprise Agent development.

A systematic Claude Code learning guide built for Chinese developers, covering ten core modules including Slash Commands, Memory, MCP, and Hooks, with a three-tier path to build an AI coding workflow in 11–13 hours.

DeepSeek R1 lacks Function Calling and JSON Output by default. Qwen3's programmable thinking modes make it the top open-source agent choice. Key LLM selection pitfalls and MCP protocol updates.

OpenAI's official open-source plugin brings Codex into Claude Code for cross-model code review. Five core features including adversarial review and sub-agent delegation — with real risk warnings included.