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国产 KVM 切换器被发现内置麦克风，原因未知，并且还被发现硬编码密码或密钥，存在严重的安全隐患。#NanoKVM 是深圳矽速推出的产品，该产品被研究人员发现存在极小的麦克风，并且 SSH 密码也是预设的，Web 登录界面的加密密钥也是硬编码的，在连接到公网后可能会被攻击。
查看全文：https://t.co/oqs4b0DQdK
AMD 似乎准备复产 #B650 芯片组，在内存价格飙升的情况下为用户提供低价选择。此前 AMD 已经证实合作伙伴将转向 B850/840，B650 这个老产品要停掉，现在内存价格太高导致 DIY 用户装机成本飙升，重新提供 B650 可以为用户提供削减成本的选择，不过 B650 也同样仅支持 DDR5 内存，用户没法复用老机器上的 DDR4 内存。
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RT @elithrar: Just shipped to the changelog — mount R2 buckets in your Containers:
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![In today's episode of programming horror...
In the Python docs of random.seed() def, we're told
"If a is an int, it is used directly." [1]
But if you seed with 3 or -3, you actually get the exact same rng object, producing the same streams. (TIL). In nanochat I was using the sign as a (what I thought was) clever way to get different rng sequences for train/test splits. Hence gnarly bug because now train=test.
I found the CPython code responsible in cpython/Modules/_randommodule.c [2], where on line 321 we see in a comment:
"This algorithm relies on the number being unsigned. So: if the arg is a PyLong, use its absolute value." followed by
n = PyNumber_Absolute(arg);
which explicitly calls abs() on your seed to make it positive, discarding the sign bit.
But this comment is actually wrong/misleading too. Under the hood, Python calls the Mersenne Twister MT19937 algorithm, which in the general case has 19937 (non-zero) bits state. Python takes your int (or other objects) and "spreads out" that information across these bits. In principle, the sign bit could have been used to augment the state bits. There is nothing about the algorithm that "relies on the number being unsigned". A decision was made to not incorporate the sign bit (which imo was a mistake). One trivial example could have been to map n -> 2*abs(n) + int(n < 0).
Finally this leads us to the contract of Python's random, which is also not fully spelled out in the docs. The contract that is mentioned is that:
same seed => same sequence.
But no guarantee is made that different seeds produce different sequences. So in principle, Python makes no promises that e.g. seed(5) and seed(6) are different rng streams. (Though this quite commonly implicitly assumed in many applications.) Indeed, we see that seed(5) and seed(-5) are identical streams. And you should probably not use them to separate your train/test behaviors in machine learning. One of the more amusing programming horror footguns I've encountered recently. We'll see you in the next episode.
[1] https://t.co/srv1ZBlDsi
[2]](/_next/image?url=https%3A%2F%2Fpbs.twimg.com%2Fmedia%2FG7sc167aMAAuZi5.png&w=3840&q=75 "In today's episode of programming horror...
In the Python docs of random.seed() def, we're told
\"If a is an int, it is used directly.\" [1]
But if you seed with 3 or -3, you actually get the exact same rng object, producing the same streams. (TIL). In nanochat I was using the sign as a (what I thought was) clever way to get different rng sequences for train/test splits. Hence gnarly bug because now train=test.
I found the CPython code responsible in cpython/Modules/_randommodule.c [2], where on line 321 we see in a comment:
\"This algorithm relies on the number being unsigned. So: if the arg is a PyLong, use its absolute value.\" followed by
n = PyNumber_Absolute(arg);
which explicitly calls abs() on your seed to make it positive, discarding the sign bit.
But this comment is actually wrong/misleading too. Under the hood, Python calls the Mersenne Twister MT19937 algorithm, which in the general case has 19937 (non-zero) bits state. Python takes your int (or other objects) and \"spreads out\" that information across these bits. In principle, the sign bit could have been used to augment the state bits. There is nothing about the algorithm that \"relies on the number being unsigned\". A decision was made to not incorporate the sign bit (which imo was a mistake). One trivial example could have been to map n -> 2*abs(n) + int(n < 0).
Finally this leads us to the contract of Python's random, which is also not fully spelled out in the docs. The contract that is mentioned is that:
same seed => same sequence.
But no guarantee is made that different seeds produce different sequences. So in principle, Python makes no promises that e.g. seed(5) and seed(6) are different rng streams. (Though this quite commonly implicitly assumed in many applications.) Indeed, we see that seed(5) and seed(-5) are identical streams. And you should probably not use them to separate your train/test behaviors in machine learning. One of the more amusing programming horror footguns I've encountered recently. We'll see you in the next episode.
[1] https://t.co/srv1ZBlDsi
[2]")
In today's episode of programming horror... In the Python docs of random.seed() def, we're told "If a is an int, it is used directly." [1] But if you seed with 3 or -3, you actually get the exact same rng object, producing the same streams. (TIL). In nanochat I was using the sign as a (what I thought was) clever way to get different rng sequences for train/test splits. Hence gnarly bug because now train=test. I found the CPython code responsible in cpython/Modules/_randommodule.c [2], where on line 321 we see in a comment: "This algorithm relies on the number being unsigned. So: if the arg is a PyLong, use its absolute value." followed by n = PyNumber_Absolute(arg); which explicitly calls abs() on your seed to make it positive, discarding the sign bit. But this comment is actually wrong/misleading too. Under the hood, Python calls the Mersenne Twister MT19937 algorithm, which in the general case has 19937 (non-zero) bits state. Python takes your int (or other objects) and "spreads out" that information across these bits. In principle, the sign bit could have been used to augment the state bits. There is nothing about the algorithm that "relies on the number being unsigned". A decision was made to not incorporate the sign bit (which imo was a mistake). One trivial example could have been to map n -> 2*abs(n) + int(n < 0). Finally this leads us to the contract of Python's random, which is also not fully spelled out in the docs. The contract that is mentioned is that: same seed => same sequence. But no guarantee is made that different seeds produce different sequences. So in principle, Python makes no promises that e.g. seed(5) and seed(6) are different rng streams. (Though this quite commonly implicitly assumed in many applications.) Indeed, we see that seed(5) and seed(-5) are identical streams. And you should probably not use them to separate your train/test behaviors in machine learning. One of the more amusing programming horror footguns I've encountered recently. We'll see you in the next episode. [1] https://t.co/srv1ZBlDsi [2]
ty to ericsilberstein1 on github for spotting the bug. https://t.co/18o1CiivgN (it's not a big bug and only comes up in the SpellingBee synthetic task evaluation but still).
