https://blog.chuanxilu.net/en/tags/ai-assisted-development/Recent content in AI-Assisted Development on Chuanxilu for Skilled Homo sapiensHugoen-USFri, 01 May 2026 10:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/05/ai-bug-root-cause-diagnosis/Fri, 01 May 2026 10:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/05/ai-bug-root-cause-diagnosis/Fix #13, and old bugs come back. This isn't a "how I fixed a bug with AI" anecdote. It's a full post-mortem of a 15+ bug battle — four rounds of attribution, regression traps, and how TDD was forced out by pain.https://blog.chuanxilu.net/en/posts/2026/04/ai-tdd-full-pipeline-from-requirements-to-code/Thu, 30 Apr 2026 14:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/04/ai-tdd-full-pipeline-from-requirements-to-code/Article 6 in the series. The previous five articles each covered one layer — requirements, design, testing, review, procedural justice. This one connects them into a working pipeline. Checklists for every stage, a real-project retrospective, and a blunt assessment of when this process is worth the overhead.https://blog.chuanxilu.net/en/posts/2026/04/adversarial-review-critical-thinking-ai-quality/Thu, 30 Apr 2026 10:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/04/adversarial-review-critical-thinking-ai-quality/Ralph Loop v0.3 encodes procedural justice into the review protocol — structured review output, critical scrutiny, contested issue protocol — so every review decision has evidence, records, and rule-based constraints. Inspired by Robert's Rules of Order, born 150 years ago.https://blog.chuanxilu.net/en/posts/2026/04/ralph-loop-ai-errors-converge/Wed, 29 Apr 2026 14:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/04/ralph-loop-ai-errors-converge/The tech spec from the previous article shrank the space where AI can improvise. But the spec itself contains assumptions — and some of them are wrong. Ralph Loop is a multi-round review protocol where an independent AI subagent audits every deliverable. Its exit condition borrows from mathematical convergence: one clean round proves nothing. Two consecutive clean rounds prove convergence.https://blog.chuanxilu.net/en/posts/2026/04/prd-to-tech-spec-ai-design-guardrails/Wed, 29 Apr 2026 10:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/04/prd-to-tech-spec-ai-design-guardrails/The second Aristotle refactor had clear requirements, clean code structure, passing tests. But the async background mechanism still did not work. The problem was not in the PRD—it was in the tech spec. This article covers what a PRD should contain, what a tech spec should add, and why both are non-negotiable when AI writes your code.https://blog.chuanxilu.net/en/posts/2026/04/why-aristotle-vibe-development-needs-gear-protocol/Sat, 25 Apr 2026 00:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/04/why-aristotle-vibe-development-needs-gear-protocol/Aristotle v1 had a one-line requirement. The reflection task ran inside the main session, polluting 371 lines of context. This article starts from that failure and walks through why requirement gaps get amplified into systematic bias in AI-assisted development, and how structured methods close those gaps.https://blog.chuanxilu.net/en/posts/2026/04/test-doc-before-test-code-reverse-anchoring/Thu, 23 Apr 2026 10:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/04/test-doc-before-test-code-reverse-anchoring/In AI-assisted development, tests are not just verification. They are the most precise requirement language you can give an AI. Drawing from my own failures, this article walks through the full chain from test scenario identification to test development documents, and explains why this method matters far more when the coder is an AI.