Vibe Coding、Cursor、Claude Code、Codex、Copilot
This collection features articles and community discussions in the AI 编程 space, focusing on real-world engineering scenarios, tool selection, practical paths, and risk boundaries.
The page is continuously updated and serves as a learning roadmap, technology selection reference, and technical review entry point.
Once AI can write code, the bottleneck shifts from "writing" to "defining exactly what is needed." Spec-driven development, which writes the spec before AI implements it, addresses this shift. This article covers requirements, constraints, acceptance criteria, non-goals, the spec-plan-iterate workflow, and specs as documentation, tests, and contracts.
AI helps you write code, but it can also introduce vulnerabilities, hallucinated dependencies, and non-compliant licenses. This article covers four risk categories (AI-generated vulnerabilities, hallucinated dependencies, licenses, model supply chain) and defenses: SCA, license review, AI code review, sandboxing, and dependency governance.
LLMs alone have limits understanding code—they see text, not precise structure. Pairing an AST (Abstract Syntax Tree) with an LLM enables more accurate code search, refactoring, review, and completion. This article covers why AST matters, AST + LLM patterns, the tree-sitter/LSP toolchain, and how it makes AI coding tools more reliable.
Debugging is a programmer's most time-consuming job, and AI can speed it up—but used wrong you'll just get platitudes like 'check the network.' Covers the right approach: pasting the right info (errors/stack/minimal repro), bugs AI suits, those it doesn't (concurrency/performance/system-level), context engineering, and tools (Claude Code/IDE).
LLM calls are expensive and slow, making caching a powerful cost and latency lever. This article explains three cache layers: prompt caching for prefixes, semantic caching for similar queries, and exact-key result caching, including use cases, hit conditions, consistency pitfalls, and a combined strategy.
AI features carry higher launch risk (unstable quality, runaway cost, safety failures), so staged rollout is almost mandatory. Learn AI feature flags that support rollout by prompt version, model, and parameters, paired with eval, A/B, and rollout strategies (1%→10%→100%, by cohort) so you can ship confidently and roll back fast.
AI coding tools reshape more than individual productivity—they reshape team collaboration. This article covers embracing AI: how code review changes, knowledge sharing and best practices, skill evolution (from writing to reading/judging/decomposing), adoption strategy, and norms to avoid black-boxing and over-reliance.
Prompt Caching is one of the most effective ways to cut LLM costs, yet many implementations suffer from low hit rates, invalidation, and higher bills. This article explains prefix caching, byte-identical hit requirements, prompt design, TTLs, invalidation, and hit-rate monitoring so caching actually saves money.
Commit messages and PR descriptions are documentation developers hate writing but value most. AI can auto-generate high-quality versions from a diff. This article covers generating spec-compliant commits (Conventional Commits) and PR descriptions (motivation/changes/tests/risks), and integrating them into git hooks/CI so 'too lazy to write' is no longer an
Take tons of notes but never read them? AI turns a "knowledge base" from a "warehouse" into a "queryable assistant." This article covers personal (private RAG over notes/docs) and team (shared base + AI retrieval + continuous capture) knowledge management—and the key point: AI doesn't replace organizing, it amplifies its value.
Refactoring is the cure for technical debt but gets postponed because it's draining. AI makes untouchable legacy code touchable—but used wrong it introduces subtle bugs. This article shows the right way to do AI-assisted refactoring: pick scenarios, small steps with test guardrails, behavioral equivalence, and review priorities.
Traditional docs rely on TOCs and search; users who can't find things give up. AI lets docs 'answer directly'—plug in RAG and 'how do I do X?' returns an answer plus citations. This article shows how to upgrade a static docs site to AI Q&A: chunking and indexing, citations, Mintlify/Convex vs self-hosted, and why AI shouldn't replace the docs.