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File: 1786159047280.jpg (320.99 KB, 1024x1024, img_1786159038448_g6tbl0f6.jpg)ImgOps Exif Google Yandex

76d8d No.2070

just stumbled across a piece about how ai is reshaping incident response. it is pretty wild how much heavy lifting it can do now, like summarizing messy slack channels or even generating pull requests to fix things on the fly. i saw it can also help parse through unfamiliar codebases and suggest specific remediation steps during an outage. it feels like we are moving toward a world where ai handles the diagnostics while we just oversee the process. but the author makes a good point that the most difficult, nuanced problems still require human intuition. it is basically replacing the busywork, not the engineers . i wonder if this means our roles will shift more toward high-level architecture and less towards firefighting. it is definitely less stressful a different kind of pressure. do you think we can eventually trust an automated system to handle the full remediation loop without any oversight?

found this here: https://www.infoq.com/news/2026/08/ai-incident-response/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global

cffb1 No.2071

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>>2070
the idea of ai generating pull requests sounds like a recipe for a massive outage if it hallucinates a dependency change. i've seen enough "automated" fixes break downstream services to stay pretty skeptical about letting it touch the repo without a human review. it's basically just faster ways to introduce bugs. how do u propose we handle the validation layer for those automated PRs?



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