#Code-Review
25 notas · última:
The compiler was supposed to end programming too
The article argues that AI will not end programming, but it will shift work toward specs, tests, review, and decisions that machines cannot make, while speeding up coding and stressing the review queue.
Why Bad Code Exists
The article says bad code usually comes from process and context: no active review, no editing or refactoring, weak technical knowledge, time pressure, fear, and lack of care or investment in quality.
How to Kill the Code Review
The essay argues that manual code review no longer scales with AI-generated code, and that human approval should move upstream to specs, constraints, and deterministic verification.
Everything I Know About Shipping Code Faster
The post argues that shipping faster comes from finding code paths quickly, keeping diffs small, testing in batches, reducing reviewer friction, and protecting focus from interruptions.
How to turn AI from a random code generator into a reliable teammate
The article argues that engineers should steer AI instead of trusting it blindly, using clear rules, review, and a steering doc so AI speeds up work without replacing judgment.
Code Review: A New Developer’s Guide
The article explains how new developers can write clearer pull requests, test code like users, and give feedback carefully so code review helps the team improve software and grow together.
You Can Grow Your Developer Intuition
The page argues that software development depends on intuition as much as analysis, and that developers can strengthen it through code review, pattern recognition, practice, and reflection.
Your job is to deliver code you have proven to work
Simon Willison argues that AI-assisted development still requires proving changes work through manual testing and automated tests, with humans accountable for the result and coding agents helping only if they can verify their own output.
The PERFECT Code Review: How to Reduce Cognitive Load While Improving Quality
The article argues that code review should be structured around business value, edge cases, reliability, design, tests, clarity, and taste to reduce cognitive load and make reviews more effective.
The obligation of a software developer
The text argues that software quality depends on technical choices and attitude: seeking the best solution, testing the code, knowing how to say no, respecting the team, doing constructive code review, and mentoring colleagues.
How to use AI for coding the right way
The author argues for using AI as programming support with context, prompts, review, and tests, while staying attentive to avoid errors, hallucinations, and lazy use.
Continuous AI in software engineering
Sean Goedecke argues AI should be used like tests and types, continuously and automatically, not as an on-demand oracle a developer has to remember to consult.
Every Layer of Review Makes You 10x Slower
Avery Pennarun argues AI coding speed doesn't fix engineering velocity because review layers, not typing speed, are the bottleneck, and draws on Deming's quality theory to argue for eliminating review stages, not stacking more of them.
AI Writes Code Faster. Your Job Is Still to Prove It Works.
Osmani's framework for code review after AI: solo devs lean on tests as a safety net, teams need a PR Contract stating intent, proof, risk, and where to focus review.
The Slow and Quiet Cognitive Atrophy of a Modern Software Engineer
AI-generated code looks clean and its own tests pass, but skipping the review step quietly erodes an engineer's own skill. Elmar Chavez traces the decay and argues discipline is what keeps understanding intact.
Write broken commits for better review
The article argues that intentionally broken commits can make code review easier when they separate mechanical changes from real ones, especially under squash merges and AI-assisted development.
Don’t Ignore Thread Safety — Design for Concurrency from Day One
The article argues that thread safety should be built into software design from the start because concurrency bugs are hard to reproduce, appear under load, and can be reduced with safer patterns and automated review.
Code Review Best Practices
The article argues that code reviews work best when they are fast, focused on readability and breaking changes, supported by automation, and preceded by a clear, small, and tested pull request.
A ‘wise’ guy tactic for effective code review practice? Use checklist templates!
The article argues that code review works better with short checklist templates. It covers automated checks, merge conflicts, commit messages, PR size, requirements, readability, coding standards, libraries, duplication, tests, and documentation.
Six tips to improve your code review skills
The article says code review works best when reviewers test changes locally, ask questions instead of making blunt statements, praise good work, label comments, separate blocking from non-blocking issues, and add extra explanation to reduce review rounds.
Pull Requests: A Primer
The article explains how to write effective pull requests by following contribution rules, keeping each PR to one scope, using clear commits and messages, and writing a concise title and body with enough context for reviewers.
Code Review Etiquette
The article explains why code reviews need shared etiquette, how harsh feedback can hurt collaboration, and how to keep critiques focused, specific, and calm.
5 Useful Tips For A Better Commit Message
The page recommends keeping the first line under 50 characters, using spell check and 72-column wrapping, avoiding git commit -m, and writing commit messages that explain why, how, side effects, and the related issue or story.
What Makes a Good Check-in?
The article argues that a good check-in should have one intent, a tight scope, a clear description, build and test consistency, completeness, review, traceability, and reversibility.
Programação em par
A programação em par é apresentada como prática do XP que reduz bugs, melhora o foco, acelera decisões e dissemina conhecimento, embora peça humildade e adaptação inicial.