Prompt Engineering

Como manter código limpo e seguro na era da IA, usando revisão humana, testes, linting e bons prompts para validar e melhorar o que a IA gera.

Prompt engineering breaks in production because prompts are fragile and hard to manage, so teams should use task-based, versioned wrappers for stable, testable AI behavior.

Explains how Claude Code hooks can auto-enforce your coding rules every session and pairs them with CodeSyncer tags to preserve architectural decisions so the AI stops forgetting.

A proof of concept showing how plain-English acceptance criteria can be turned into runnable Playwright tests via MCP, keeping testers owning intent without needing to code.

AI 코딩 도구는 숙련 개발자를 오히려 19% 느리게 만들 수 있으며, 이제는 알고리즘 실력보다 AI 한계 파악·문맥 이해·프롬프트/커뮤니케이션 같은 ‘바이브코딩’ 역량이 중요하다는 내용이다.

AgentSkills.to is an open-source marketplace of 3,700+ plug-and-play, production-ready AI agent skills that work across tools like Claude Code, Cursor, and Codex CLI to speed up coding.

It explains that giving Claude evocative, principle-based UI guidance (not overly prescriptive rules) produces more thoughtful, consistent interface designs instead of generic safe patterns.

The post explains “vibe coding,” where you describe what you want and AI writes the code, speeding work but raising risks like shallow understanding, tech debt, and security—so developers must shift from typing syntax to designing and reviewing systems.

The author explains shifting from trusting AI outputs to auditing their assumptions, logic, and omissions so decisions stay accurate and accountable.

Stanford researchers share three practical ways to reduce time wasted on confusing, low-quality AI-generated content and explain who’s responsible.

A simple guide to writing better AI prompts by setting a role, clear goals and formats, examples, creativity controls, and validation to get faster, higher-quality outputs.

Anthropic’s new Claude “Constitution” replaces brittle rule-based safety with a layered, reasoning-driven value system that makes the model more context-aware, predictable, and developer-friendly.

It explains how to prevent bloated, conflicting AI agent prompts by classifying fixes as temporary model patches vs permanent business facts and purging the temporary ones during model upgrades.

This explains why highlighting feels like learning but isn’t, and offers an AI prompt that turns an LLM into a study coach using active recall, mnemonics, testing, and spaced repetition.

Stop stuffing huge rule files into your AI context window; split rules into on-demand “skills” so only relevant guidance loads and the model stays focused.

The piece explains how pressure-testing AI at work—under tight deadlines and high stakes—reveals real skill through clear framing, quick validation, and taking ownership instead of hiding behind automation.

A builder explains how switching from SD3.5 to a typography-focused Ideogram pipeline fixed illegible text-in-image and enabled shipping reliable marketing card designs.

Explains that AI models are stateless and only use provided context, so systems must store and manage memory and state externally to avoid inconsistent, hard-to-debug behavior.

Gudong Cover is a free, open-source web app that uses AI to quickly generate and download WeChat and Xiaohongshu cover images from your title or content.

Explains three easy prompt-injection attacks you can try on popular chatbots and why AI apps must assume jailbreaks will happen and use layered defenses.