RE: https://chaos.social/@moreentropy/116684911900807152

> Now if any of the people posting the rage stuff want to actually review any of the code I’ve published and make constructive criticisms then that would be great!

got a point there...

#OpenSource #CodeReview

Your AI coding assistant can make something look launch-ready before it is actually safe.

CheckYourself is the reality check: boundaries, validation, token handling, auth, rollback receipts, and the backlog your assistant should have made before you shipped.

Audit the code before the user's audit.

#AICoding #CodeReview #DevTools #SoftwareEngineering #KyaniteLabs

Watch: https://www.youtube.com/watch?v=_8f1Drg3RMQ

https://www.youtube.com/watch?v=_8f1Drg3RMQ

CheckYourself: Audit The Code Before The User's Audit #Shorts

YouTube

Give me the code review skills that you like (UI / FE, BE, Devops etc.).

#llm #skills #skill #codereview #review #ai

#CodeReview and #Sustainability in the #DigitalHumanities Humanities Publication Landscape

Are you developing code for Digital Humanities research? Have you ever thought about publishing your code? Do you wish code would be more reusable, visible, and impactful? Join our webinar to explore how code review and publication practices could transform how we think about code written for research.

When? June 4, 2026 at 10am ET/4pm CET

Where? On Zoom, register here: https://asu.zoom.us/meeting/register/heP_utGnSBScRLtGGXlwZg

Welcome! You are invited to join a meeting: DHTech June Meetup. After registering, you will receive a confirmation email about joining the meeting.

Are you developing code for Digital Humanities research? Have you ever thought about publishing your code? Do you wish code would be more reusable, visible, and impactful? Join our upcoming webinar to explore how code review and publication practices could transform how we think about code written for research. We’ll discuss how publication requirements contribute to reusability and sustainability of code written for research and how they increase the visibility of code as a valuable part of research output.

Zoom

Anthropic standardisiert den Einsatz von KI-Agenten zur Schwachstellensuche mit einem sechsstufigen Best-Practice-Prozess.

Ein Multi-Agenten-System trennt die initiale Codesuche strikt von einem zweiten Prüf-Agenten, der Funde in isolierten MicroVMs verifiziert. Das Modell generiert abschließend Patches inklusive Vorab-Tests.

#Anthropic #ClaudeOpus #CodeReview #CyberSecurity #AIGeneratedImage

https://www.all-ai.de/tutorials/tutorials-ki/anthropic-best-practices-code-1

Anthropic präsentiert »Best Practices« für die Überprüfung von Code.

Modelle wie Claude Opus oder Mythos decken Fehler rasant auf. Die wahre Herausforderung bleibt jedoch die schnelle Behebung.

all-ai.de
🤖💻 Can AI support industrial code review?
At #ICST2026 in Daejeon 🇰🇷, Meltem Aksoy presented RC Trust research co-authored with Igli Begolli and Daniel Neider on fine-tuned language models for real-world C# code review.
The key takeaway: AI can improve efficiency and support routine tasks – but human expertise remains essential for complex decisions. 🧠🤝
https://rc-trust.ai/news/news-detail/smarter-code-review-still-needs-humans
#TrustworthyAI #SoftwareTesting #CodeReview #ReliableAI

The Hidden Cost of Nice Syntax: When Angular's New Template Features Make Code Harder to Reason About

Angular's newer template syntax is powerful, but spread/rest, arrow functions, multi-case switches, and richer control flow need team rules before clever templates become maintenance debt.

#Angular #Frontend #TypeScript #SoftwareArchitecture #CodeReview #Maintainability

https://g.omid.dev/5O8Qglh

The Hidden Cost of Nice Syntax: When Angular's New Template Features Make Code Harder to Reason About

Angular's newer template syntax is powerful, but spread/rest, arrow functions, multi-case switches, and richer control flow need team rules before clever templates become maintenance debt.

Un papier arXiv récent a étudié 302 600 commits vérifiés comme écrits par IA dans 6 299 repos GitHub.

Il trouve 484 366 problèmes distincts par analyse statique, et 22,7% des problèmes suivis survivent jusqu'à la version la plus récente.

Ça ne veut pas dire "l'IA écrit du mauvais code". Un humain introduit aussi de la dette. Le point : l'IA baisse la friction à produire du plausible. Le goulot se déplace vers la qualification.

#SoftwareEngineering #TechnicalDebt #AI #CodeReview