110 karma · joined July 7, 2025
If instead the purpose of your website is to manipulate users for financial gain (for instance by showing media attempting to manipulate their purchasing decisions, after receiving a bribe from a vendor), and the information is just a way to lure users, then maybe this malicious business model will finally be no longer possible.
The way one would backdoor something like Bitlocker is to encrypt the disk encryption key with a (post-quantum) public key for which only the backdoor owner has the private key for, and then put it on a place on disk that is unused by the filesystem.
The only important system that uses it as a security boundary is Android and there is mitigated by the fact that APKs need user approval, plus strict SELinux and seccomp policy plus the GrapheneOS hardening, and in this case the mitigations succeeded (https://discuss.grapheneos.org/d/35110-grapheneos-is-protect...)
Absurdly high price for a novel device of unclear utility (a VR headset but incompatible with all existing VR software) resulting in few users.
No support for PC VR nor Android/Quest VR apps resulting in little software, no massive investment in getting Vision Pro specific software written, little interest in porting due to the few users.
It's a set of biases installed in people, whose purpose is mostly to replicate themselves.
Humans are MORE susceptible that LLMs, because LLMs's biases are easily steered to something else, unlike most humans.
If you ask a human why they did something, the answer is a guess, just like it is for an LLM.
That's because obviously there is no relationship between the mechanisms that do something and the ones that produce an explanation (in both humans and LLMs).
An example of evidence from Wikipedia, "split brain" article:
The same effect occurs for visual pairs and reasoning. For example, a patient with split brain is shown a picture of a chicken foot and a snowy field in separate visual fields and asked to choose from a list of words the best association with the pictures. The patient would choose a chicken to associate with the chicken foot and a shovel to associate with the snow; however, when asked to reason why the patient chose the shovel, the response would relate to the chicken (e.g. "the shovel is for cleaning out the chicken coop").[4]
1. Anthropic has not published anything about why they made the change and how exactly they changed it
2. Nobody has reverse engineered it. It seems easy to do so using the free token counting APIs (the Google Vertex AI token count endpoint seems to support 2000 req/min = ~3million req/day, seems enough to reverse engineer it)
If you want filenames, you need to request access to a directory, not to an image
Here is my attempt. I think they should be optimal up to around 15 eml.nodrs, the latter might not be:
# 0
1=1
# 1
exp(x)=eml(x,1)
e-ln(x)=eml(1,x)
e=exp(1)
# 2
e-x=e-ln(exp(x))
# 3
0=e-e
ln(x)=e-(e-ln(x))
exp(x)-exp(y)=eml(x,exp(exp(y)))
# 4
id(x)=e-(e-x)
inf=e-ln(0)
x-ln(y)=eml(ln(x),y)
# 5
x-y=x-ln(exp(y))
-inf=e-ln(inf)
# 6
-ln(x)=eml(-inf,x)
ln(ln(x))=ln(ln(x))
# 7
-x=-ln(exp(x))
-1=-1
x^-1=exp(-ln(x))
ln(x)+ln(y)=e-((e-ln(x))-ln(y))
ln(x)-ln(y)=ln(x)-ln(y) # using x - ln(y)
# 8
xy=exp(ln(x)+ln(y))
x/y=exp(ln(x)-ln(y))
# 9
x + y = ln(exp(x))+ln(exp(y))
2 = 1+1
# 10
ipi = ln(-1)
# 13
-ipi=-ln(-1)
x^y = exp(ln(x)y)
# 16
1/2 = 2^-1
# 17
x/2 = x/2
x2 = x2
# 20
ln(sqrt(x)) = ln(x)/2
# 21
sqrt(x) = exp(ln(sqrt(x)))
# 25
sqrt(xy) = exp((ln(x)+ln(y))/2)
# 27
ln(i)=ln(sqrt(-1))
# 28
i = sqrt(-1)
-pi^2 = (ipi)(ipi)
# 31
pi^2 = (ipi)(-ipi)
# 37
exp(xi)=exp(xi)
# 44
exp(-xi)=exp(-(xi))
# 46
pi = (ipi)/i
# 90+x?
2cos(x)=exp(xi)+exp(-xi))
# 107+x?
cos(x) = (2cos(x))/2
# 118+x?
2sin(x)=(exp(x*i)-exp(-xi))/i # using exp(x)-exp(y)
# 145+x?
sin(x) = (2sin(x))/2
# 217+3x?
tan(x) = 2sin(x)/(2cos(x))
This needs to support at least Gemini CLI, Codex and OpenCode as well, preferably by being generic as much as possible.
Also the fact that it doesn't detect locking the same mutex twice makes no sense: a static order obviously detects that and when locking multiple mutexes at the same level all you need to do is check for equal consecutive addresses after sorting, which is trivial.
Overall it seems like the authors are weirdly both quite competent and very incompetent. This is typical of LLMs, but it doesn't seem ZlLM-made.
Perhaps the issue it that each core has a locked cacheline entry for each other core, but even then given the size of current CPUs doubling it shouldn't be that significant. And one could also add just a single extra entry and then have a global lock but that only locks the ability to lock a second cacheline.
And afaik HDLs are almost exclusively used for hardware synthesis, never seen any software written in those languages.
So it doesn't seem important at all. In fact, for software simulation of hardware you'd want the simulation to randomly choose anything possible in hardware, so the Verilog approach seems correct.