over-analyze everything.
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Joined March 2025
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PHP2Shell technical breakdown to show you how an attack could actually run code remotely on PHP servers by single HTTP POST request crafted to cause OOM error to exploit the destructor callback.
AI adoption is becoming ever more important as it's ability to find security vulnerabilities is currently greater than our ability to put the patch push through.
In this case, PHP2Shell was known for 10 weeks until multiple leaks forced them to frantically patch the update earlier than scheduled. What a nightmare.
so this whole PHP shenanigans is really more human error in not being able to keep the sec vulnerability under wraps than about AI mayhem causing havoc.
this could have gone very differently if we held it together.
Quick update on Fred (that's his name now), he seems to have reverted to The Thinker pose after 9 hours of training with SAC.. i may revert back to PPO
xitter.cf/calebfoundry/status/21…
DeepSeeek just open sourced DeepInfer that only supports NVIDIA Blackwell (data center GPUs not consumer chips like 5090) and Ascend 950 NPUs for now..
This is targeted directly as data center inference efficiency and they report that at the cheapest API cost for running DeepSeek V4.1 Flash, the hourly revenue/GPU was $38 capable of making 155 trillion tokens per day at a cluster level.
The downstream effect for efficiency at the infra level is lower cost of intelligence for consumer further driving down cost given higher inference margins.
github.com/deepseek-ai/DeepI…
This week has been an incredible run for open source AI.
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source!
Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.
🔗 github.com/deepseek-ai/DeepI…
1/4
UPDATE: my constipated agent is doing better.
xitter.cf/calebfoundry/status/21…
RL scaling for MiMo-V2.6 explained.
Great work @XiaomiMiMo and thank you for sharing your knowledge and experience openly to the world.
At this point, should we just assume that if Chinese labs DON'T mention which GPU they used to train their models, they most likely used NVIDIA chips?
Today I learned that higher batch size doesn't always mean higher training efficiency.
There's a thing called critical batch size which is the point where batch size increase has diminishing return because gradients contain enough information for the optimizer and adding more data (which means higher compute tradeoff) starts to become less worth it.
