Former senior tech manager at Meituan, ex‑tech director at New Oriental, with experience at JD.com and Qunar; specializes in Java interview coaching and regularly shares hardcore technical content. Runs a video channel of the same name.
The article argues that declaring LLM MCP obsolete while praising Skills as the ultimate capability reflects a beginner’s misunderstanding, explaining that MCP is a low‑level tool‑connection protocol akin to USB/HTTP, whereas Skills are high‑level business‑logic wrappers, and the real engineering challenges lie elsewhere.
The article showcases GPT-Image-2's impressive ability to generate accurate visual and textual content from prompts, explains how its structural understanding resolves previous AI image flaws, and analyzes the disruptive impact on the design industry, including job displacement, cost efficiency, and market oversupply.
This guide presents five practical, intermediate‑level techniques for using OpenAI's Codex—writing explicit prohibitions, breaking tasks into fine‑grained steps, generating multiple solutions with a "Best‑of‑N" approach, analyzing before coding, and prioritizing requirements—to help developers steer AI assistance toward reliable, low‑risk code changes.
This article explains what vectors are, why traditional relational databases struggle with high‑dimensional similarity queries, and how the open‑source Milvus vector database efficiently stores, indexes, and retrieves massive vectors for AI applications such as semantic search, image matching, and recommendation.
The article explains why simply switching to cheaper large language models fails in interviews and outlines five practical techniques—prompt simplification, context management, output control, model tiering, and caching—to reduce token consumption while preserving answer quality.
The article outlines a practical, three‑category approach to answering the most common AI‑coding interview question, warning against vague praise and detailing how to handle engineering gaps, performance and concurrency pitfalls, and safe modifications of legacy code using structured prompts and thorough code review.
This article walks through creating a LangGraph workflow in Python, first using OpenAI’s GPT‑5‑nano model, then swapping to Alibaba’s Qwen 3.5‑plus model, showing how to suppress warnings, filter out thinking responses, visualize the graph, and troubleshoot common errors, all without any prior AI coding experience.
The MySQL optimizer can select an inappropriate index due to inaccurate statistics or sampling errors, but you can correct this by refreshing statistics with ANALYZE TABLE, increasing sample pages, or explicitly forcing the right index using the FORCE INDEX hint.
This guide explains the default Java ThreadPoolExecutor workflow, identifies why interview questions may require launching maximum threads before queuing tasks, and presents a concise two‑pool plus custom rejection‑policy solution that satisfies the requirement without writing a custom thread pool from scratch.
When the core pool size of a Java ThreadPoolExecutor is configured to zero, new tasks are initially placed into the work queue, and the executor may create non‑core threads to process them, a behavior that often surprises interview candidates.
