Backend tech guide and AI engineering practice covering fundamentals, databases, distributed systems, high concurrency, system design, plus AI agents and large-model engineering.
The article explains the built‑in Claude Code /simplify command, how it uses three parallel AI agents to review and automatically fix code, demonstrates real‑world bugs it uncovered in Java projects, compares it with traditional linters, and offers practical tips and integration guidance.
This article explores the Learn Claude Code website, breaking down the universal agent loop into twelve incremental versions, demonstrating language‑agnostic implementations in Python and Java, and detailing progressive capabilities—from basic tool integration to memory compression, concurrency, and multi‑agent collaboration.
This article demonstrates how the Qoder AI plugin for JetBrains IDEA can transform lengthy backend tasks—such as deep‑pagination optimization and legacy code refactoring—into rapid, automated workflows, cutting development time from days to under ten minutes while preserving code quality.
While many believe that using SELECT * inevitably disables indexes, MySQL’s optimizer only skips indexes when a full table scan is cheaper, and patterns such as violating the left‑most prefix, applying functions or implicit casts, leading‑wildcard LIKE, OR/IN misuse, and mismatched ORDER BY can cause index invalidation, prompting some companies to forbid SELECT * in production.
The author, a heavy user of AI agents for daily code refactoring, compares the expensive Opus 4.6 with the budget‑friendly MiniMax M2.5, showing how a mixed‑model strategy cuts costs dramatically while maintaining speed and quality across two full‑stack development case studies.
This article explains why ThreadLocal values are not automatically transferred in asynchronous or thread‑pool contexts, compares JDK's InheritableThreadLocal with Alibaba's TransmittableThreadLocal (TTL), and shows how TTL captures, replays, and restores context safely with code examples and diagrams.
The author explains why state-owned enterprises are not always stable, why their low‑tech projects hinder interview performance, and offers concrete steps—such as adding Redis caching, multithreading, and using the STAR method—to turn a modest Java background into compelling interview material.
During a Spring‑based interview platform project, the author discovered four typical bugs introduced by Claude Code—transactional self‑invocation loss, AI‑generated NullPointerExceptions, async task failures after entity deletion, and Redis Stream message buildup—and explains the root causes and concrete remediation steps.
IntelliJ IDEA 2025.3.1 introduced severe Maven project initialization memory leaks and WSL2 file‑system configuration resets, causing IDE freezes and OOM crashes, but JetBrains addressed these issues in the 2025.3.1.1 update and provides temporary workarounds such as cache cleanup, icon disabling, and registry tweaks.
After testing MiniMax M2.1 on a full‑stack e‑commerce order‑management task, the author finds the model correctly implements DDD layers, transaction handling, custom exceptions, and even generates a polished cyber‑punk UI, while its Java‑to‑Go code respects each language’s concurrency model and scores over 88 on the VIBE benchmark.
