Філіп Джей Фрай

Іди нахуй

[Філіп Джей Фрай @zradnyk]

@paniNpani Те саме, життя не проносилось перед очима, якщо шо 😅

[Філіп Джей Фрай @zradnyk]

Мадяр хохляцкий ✍️

[Філіп Джей Фрай @zradnyk]

@0x_RRP @killl_debil Чуєш ти

[ZAORALEK PROJECT @tvoya__mala]

@TaubeKonrad @nicolange_

[Філіп Джей Фрай @zradnyk]

Боже як ахуєнно

[NO CONTEXT HUMANS @HumansNoContext]

He is exactly who he thinks he is

[Bueno @0xBueno_]

VHS cassette packaging design was on another level

[Філіп Джей Фрай @zradnyk]

@FM_Szijjarto шо ти, галава?

[Філіп Джей Фрай @zradnyk]

@0x_RRP @OginyaGerc

[Філіп Джей Фрай @zradnyk]

@_Sovinskiy Сука, буквально

[Філіп Джей Фрай @zradnyk]

@DruzheKarlik Вєлікій

[Філіп Джей Фрай @zradnyk]

@0x_RRP @JulianRoepcke @Deepstate_UA Програв

[Aakash Gupta @aakashgupta]

The scariest number here: 3.61% of CPUs in one large-scale study were found to cause silent data corruptions. Not “a few bad chips.” Nearly 4 out of every 100 processors doing math wrong, silently, with no error log.

Google coined the term “mercurial cores” in 2021 after their production teams kept blaming software for data corruption. They’d debug for weeks, find nothing wrong with the code, swap the machine, problem gone. The actual cause: manufacturing defects at sub-7nm that pass every factory test, then degrade unpredictably months or years after deployment.

Facebook confirmed the same thing independently. Hundreds of affected CPUs across hundreds of thousands of machines. The defect doesn’t crash your system. It just gives you 5 instead of 6 when you multiply 2x3, under specific microarchitectural conditions, with zero indication anything went wrong.

Now think about what this means for AI training. A single corrupted GPU or CPU in a distributed training cluster doesn’t just produce one bad output. It feeds corrupted gradients into a synchronization step that gets averaged across every accelerator in the cluster. One bad chip can silently poison an entire training run. NVIDIA published a whitepaper on exactly this problem. Loss spikes during LLM training that nobody could explain traced back to silent hardware corruption.

The part that keeps infrastructure engineers up at night: traditional defenses don’t work. ECC memory can’t catch this because the corruption happens during computation, not storage. Checksums like CRC heavily use vector operations, which are themselves one of the most vulnerable instruction types. The tools designed to detect corruption are running on the same flawed silicon.

Google’s current detection method? Roughly half human-driven, half automated. And of the machines humans flag as suspicious, only about 50% are actually confirmed mercurial on deeper investigation. We’re debugging trillion-parameter models on hardware where we can’t reliably tell which chips are lying to us.

Moore’s Law gave us more transistors. It also gave us transistors we can’t fully verify.

[Філіп Джей Фрай @zradnyk]

@FMJ_Grif А чого номери на жетонах заклеєні? 🤔

[Філіп Джей Фрай @zradnyk]

@katalina_ada

[Орест @kztsky]

@ElinaSvitolina ми все одно Вас дуже любимо ❤️

[Сашко @winchsashka]

Якщо ви чули вибух, це просто я репнув боже

[Bundeskanzler Friedrich Merz @bundeskanzler]

Russland eskaliert mit dem Einsatz einer Oreschnik-Rakete gegen zivile ukrainische Energieinfrastruktur in Lwiw erneut, während Ukraine, USA und Europa sich für Frieden einsetzen. Drohgebärden sollen Angst machen, aber sie werden nicht wirken. Wir stehen an der Seite der Ukraine.