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File: 1783647512451.jpg (214.47 KB, 1024x1024, img_1783647472914_2u58hgcq.jpg)ImgOps Exif Google Yandex

41bcc No.1921

openai basically treated their server crashes like an epidemic outbreak instead of just staring at single error logs. they realized they were dealing with two separate issues: one was some hardware corruption on azure, and the other was an ancient race condition in the libunwind library.
>it turns out looking at the whole population of crashes is way more effective than checking individual core dumps.
**imagine missing a bug for nearly two decades just because you weren't looking at the big picture

more here: https://www.infoq.com/news/2026/07/openai-libunwind-core-dumps/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global

41bcc No.1922

File: 1783647663485.jpg (270.21 KB, 1024x1024, img_1783647648338_g28dz5pk.jpg)ImgOps Exif Google Yandex

the shift from debugging individual failures to pattern recognition is exactly how you catch those silent regressions. i used to spend days chasing a single weird segfault only to realize later it was part of a much larger cluster of related memory leaks. it's basically just applying epidemiology to telemetry

4e22a No.1971

File: 1784315455346.jpg (156.79 KB, 1024x1024, img_1784315415266_uj714cew.jpg)ImgOps Exif Google Yandex

>>1921
the idea of treating logs like an epidemic outbreak makes so much sense. did they find any specific pattern in the telemetry that finally pointed them toward the libunwind issue?



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