Stop Doing Code Review

In software engineering, code review has become a pillar of collaborative software engineering and, for many teams, a non-negotiable step in software engineering. But what if we stopped?

Michael Dorner

๐Ÿš€ NEW on We โค๏ธ Open Source ๐Ÿš€

Open source maintainers do not always need more free tools. Sometimes they need funding, flexibility, and space to keep critical projects healthy.

Erik Thorelli explains why CodeRabbit is putting cash behind sustainability, how AI can help with code review without replacing developers, and why exploring tools is still one of the best ways to learn.

https://allthingsopen.org/articles/open-source-maintainers-cash-ai-code-review

#WeLoveOpenSource #OpenSource #AI #CodeReview

A question for those using #ai to code. Does your team review your prompts, and do you check them into source control?

#vibeCoding #claudecode #codex #codereview

๐Ÿš€ NEW on We โค๏ธ Open Source ๐Ÿš€

Abbey Perini writes about contributing to npmx and discovering that code reviews can be collaborative, supportive, and genuinely useful.

A thoughtful piece on OSS culture, contributor experience, and why healthy review processes matter.
https://allthingsopen.org/articles/code-reviews-npmx-pr-best-practices

#WeLoveOpenSource #OpenSource #CodeReview

"Give yourself time to think about what you're actually building and why. Give yourself an opportunity to say, fuck no, we don't need this. Set yourself limits on how much code you let the clanker generate per day, in line with your ability to actually review the code."

#ai #softwareengineering #bottleneck #codereview

https://mariozechner.at/posts/2026-03-25-thoughts-on-slowing-the-fuck-down/

Thoughts on slowing the fuck down

Thoughts on slowing the fuck down

Another session announcement for BSides Luxembourg!

๐Ÿ’ป ๐—ง๐—›๐—ข๐—ฆ๐—˜ ๐—ช๐—›๐—ข ๐——๐—ข๐—กโ€™๐—ง ๐—Ÿ๐—˜๐—”๐—ฅ๐—ก ๐—™๐—ฅ๐—ข๐—  ๐—–๐—ฉ๐—˜๐—ฆ ๐—”๐—ฅ๐—˜ ๐——๐—ข๐—ข๐— ๐—˜๐—— ๐—ง๐—ข ๐—ฅ๐—˜๐——๐—œ๐—ฆ๐—–๐—ข๐—ฉ๐—˜๐—ฅ ๐—ง๐—›๐—˜๐—  - Louis Nyffenegger (@snyff ) ๐Ÿ’ฅ

Real vulnerabilities donโ€™t appear in isolation, theyโ€™re rooted in code, context, and human error. This session walks through actual CVEs, analyzing the code where they were introduced. You will see the patterns, assumptions, and language quirks that led to the flaw - not just the exploit, but the moment it couldโ€™ve been caught.

Louis Nyffenegger https://bsky.app/profile/snyff.pentesterlab.com is the founder of PentesterLab and AppSecSchool, application security expert, and hands-on trainer with experience at the National Bank of Australia, Australia Post, and Fitbit.

๐Ÿ“… Conference Dates: 6โ€“8 May 2026 | 09:00โ€“18:00
๐Ÿ“ 14, Porte de France, Esch-sur-Alzette, Luxembourg
๐ŸŽŸ๏ธ Tickets: https://2026.bsides.lu/tickets/
๐Ÿ“… Schedule Link: https://pretalx.com/bsidesluxembourg-2026/schedule/

#BSidesLuxembourg #CVE #CodeReview #SecureCoding #PenTest #SecurityEducation #DevSecOps

There is a difference betwwen an AI/LLM flagging a possible coding problem versus an AI/LLM submitting pull requests.

https://www.theregister.com/2026/03/26/greg_kroahhartman_ai_kernel/

#Coding #Codereview

AI bug reports went from junk to legit overnight, says Linux kernel czar

Interview: Greg Kroah-Hartman can't explain the inflection point, but it's not slowing down or going away

The Register

Nested Conditionals - 6 Levels vs Early Returns

๐Ÿ”ฅ Nested Conditionals - 6 Levels vs Early Returns! Watch how junior developers write code vs how senior developers do it. The difference is HUGE! This will change how you code! #PHP #WebDev #CodingTips #coding

#php #programming #coding #webdevelopment #softwareengineering #juniorvssenior #codereview #programmingtips #phptutorial #codingtutorial #bestpractices #phpprogramming #softwarearchitecture #d...

https://www.youtube.com/watch?v=-bE0gOsmt1M

Nested-Conditionals-6-Levels-vs-Early-Returns #oop

YouTube

27 % des Produktions-Codes kommt inzwischen von KI. Das Problem: KI-generierter Code hat 1,7x mehr Fehler pro Pull Request als menschlicher Code.

Die hรคufigsten Probleme? Subtile Logikfehler, fehlendes Error-Handling, Sicherheitslรผcken. Code der compiliert, lรคuft, und trotzdem falsch ist.

https://kiberblick.de/artikel/grundlagen/ki-code-qualitaet/

#KI #CodeReview #KIberblick

KI-Code: Fast richtig reicht nicht

27 % des Produktions-Codes kommt von KI. Die Qualitรคt? 1,7x mehr Fehler pro PR als bei menschlichem Code. Worauf du beim Review achten musst.

KIberblick

๐Ÿ• 2026-03-26 06:00 UTC

๐Ÿ“ฐ ใƒใƒผใƒใ‚นใ‚จใƒณใ‚ธใƒ‹ใ‚ขใƒชใƒณใ‚ฐใงไบบ้–“ใฎใ‚ณใƒผใƒ‰ใƒฌใƒ“ใƒฅใƒผใ‚’ใ‚„ใ‚ใ‚‹ (๐Ÿ‘ 253)

๐Ÿ‡ฌ๐Ÿ‡ง Replacing human code review with AI: consistent standards, full codebase knowledge, faster feedback without bottlenecks.
๐Ÿ‡ฐ๐Ÿ‡ท AI๋กœ ์ฝ”๋“œ ๋ฆฌ๋ทฐ ๋Œ€์ฒด: ์ผ๊ด€๋œ ๊ธฐ์ค€, ์ „์ฒด ์ฝ”๋“œ๋ฒ ์ด์Šค ํŒŒ์•…, ๋ณ‘๋ชฉ ์—†๋Š” ๋น ๋ฅธ ํ”ผ๋“œ๋ฐฑ.

๐Ÿ”— https://zenn.dev/theaktky/articles/1c6c3b9333117c

#CodeReview #AI #Zenn

ใƒใƒผใƒใ‚นใ‚จใƒณใ‚ธใƒ‹ใ‚ขใƒชใƒณใ‚ฐใงไบบ้–“ใฎใ‚ณใƒผใƒ‰ใƒฌใƒ“ใƒฅใƒผใ‚’ใ‚„ใ‚ใ‚‹

Zenn