math be like:

"Addition (often signified by the plus symbol "+") is one of the four basic operations of arithmetic; the others are subtraction, multiplication and division. The addition of two whole numbers is the total amount of those values combined. For example, in the adjacent picture, there is a combination of three apples and two apples together, making a total of five apples. This observation is equivalent to the mathematical expression "3 + 2 = 5" i.e., "3 add 2 is equal to 5".

Besides counting items, addition can also be defined on other types of numbers, such as integers, real numbers and complex numbers. This is part of arithmetic, a branch of mathematics. In algebra, another area of mathematics, addition can be performed on abstract objects such as vectors and matrices.

Addition has several important properties. It is commutative, meaning that order does not matter, and it is associative, meaning that when one adds more than two numbers, the order in which addition is performed does not matter (see Summation). Repeated addition of 1 is the same as counting; addition of 0 does not change a number. Addition also obeys predictable rules concerning related operations such as subtraction and multiplication.

Performing addition is one of the simplest numerical tasks. Addition of very small numbers is accessible to toddlers; the most basic task, 1 + 1, can be performed by infants as young as five months and even some members of other animal species. In primary education, students are taught to add numbers in the decimal system, starting with single digits and progressively tackling more difficult problems. Mechanical aids range from the ancient abacus to the modern computer, where research on the most efficient implementations of addition continues to this day."

And you think like .. easy, but then you turn the page:

Everytime people talk about big data/ operations research

python machine learning tutorials:
- import preprocessed dataset in perfect format specially crafted to match the model instead of reading from file like an actual real life would work
- use images data for recurrent neural network and see no problem
- use Conv1D for 2d input data like images
- use two letter variable names that only tutorial creator knows what they mean.
- do 10 data transformation in 1 line with no explanation of what is going on
- just enter these magic words
- okey guys thanks for watching make sure to hit that subscribe button

ehh, the machine learning ecosystem is burning pile of shit let me give you some examples:

- thanks to years of object oriented programming research and most wonderful abstractions we have "loss.backward()" which have no apparent connection to model but it affects the model, good to know
- cannot install the python packages because python must be >= 3.9 and at the same time < 3.9
- runtime error with bullshit cryptic message
- python having no data types but pytorch forces you to specify float32
- lets throw away the module name of a function with these simple tricks:
"import torch.nn.functional as F"
"import torch_geometric.transforms as T"
- tensor.detach().cpu().numpy() ???
- class NeuralNetwork(torch.nn.Module):
def __init__(self):
super(NeuralNetwork, self).__init__() ????
- lets call a function that switches on the tracking of math operations on tensors "model.train()" instead of something more indicative of the function actual effect like "model.set_mode_to_train()"
- what the fuck is ".iloc" ?
- solving environment -/- brings back memories when you could make a breakfast while the computer was turning on
- hey lets choose the slowest, most sloppy and inconsistent language ever created for high performance computing task called "data sCieNcE". but.. but. you can use numpy! I DONT GIVE A SHIT about numpy why don't you motherfuckers create a language that is inherently performant instead of calling some convoluted c++ library that requires 10s of dependencies? Why don't you create a package management system that works without me having to try random bullshit for 3 hours???
- lets set as industry standard a jupyter notebook which is not git compatible and have either 2 second latency of tab completion, no tab completion, no documentation on hover or useless documentation on hover, no way to easily redo the changes, no autosave, no error highlighting and possibility to use variable defined in a cell below in the cell above it
- lets use inconsistent variable names like "read_csv" and "isfile"
- lets pass a boolean variable as a string "true"
- lets contribute to tech enabled authoritarianism and create a face recognition and object detection models that china uses to destroy uyghur minority
- lets create a license plate computer vision system that will help government surveillance everyone, guys what a great idea

I don't want to deal with this bullshit language, bullshit ecosystem and bullshit unethical tech anymore.

In last episode of "How SystemD screwed me over", we talked about Systemd's PrivateTMP and how it stopped me from generating SSL certificates.

In today's episode - SystemD vs CGroups!

Mister Pottering and his team apparently felt that CGroups are underused (As they can be quite difficult to set up), and so decided to integrate them into SystemD by default. As well as to provide a friendlier interface to control their values.

One can read about these interactions in the manual page "systemd.resource-control"

All is cool so far. So what happened to me today?

Imagine you did a major system release upgrade of a production server, previously tested on a standalone server. This upgrade doesn't only upgrade the distribution however, it also includes the switch from SysVInit to SystemD. Still, everything went smooth before, nothing to worry now then, right? Wrong.

The test server was never properly stress-tested. This would prove to be an issue.

When the upgrade finishes, it is 4 AM. I am happy to go to bed at last. At 6 AM, however, I am woken up again as the server's webservices are unavailable, and the machine is under 100% CPU load. Weird, I check htop and see that Apache now eats up all 32 virtual cores. So I restart it, casting it off to some weird bug or something as the load returns to normal.

2 hours later, however, the same situation occurs. This time, I scour all the logs I can, and find something weird - Many mentions that Apache couldn't create a worker thread? That's weird.

Several hours of research and tinkering later, I found out the following:
1 - By default, all processes of a system that runs SystemD are part of several CGroups. One of these CGroups is the PID CGroup, meant to stop a runaway process from exhausting all PIDs/TIDs of a system.

This limit is, by default, set to a certain amount of the total available PIDs. If a process exhausts this limit, it can no longer perform operations like fork().

So now, I know the how and why, but how should I solve this? The sanest option would be to get a rough estimate of just how many threads the Apache webserver might need. This option, though, is harder, than apparent. I cannot just take the MaxRequestsWorkers number... The instance has roughly double the amount of threads already. The cause being, as I found out, the HTTP/2 module, which spawns additional threads that do not count towards this limit. So I have no idea what limit to set.

Or I could... Disable the limit for just the webserver via the TasksAccounting switch. I thought this would work. And it did seem to... Until I ran out of TIDs again - Although systemctl status apache2.service no longer reported the number of tasks or a task limit of the process, the PID CGroup stayed set to the previous limit. Later I found out that I can only really disable the Task Accounting for all the units of a given slice and its parents.

This, though, systemctl somewhat didn't make apparent (And I skimmed the manual, that part was my fault)

So... The only remaining option I had was to... Just set the limit to infinite. And that worked, at last.

It took me several hours to debug this issue. And I once again feel like uninstalling systemd again, in favor of sysvinit.

What did I learn? RTFM, carefully, everything is important, it is not enough to read *half* the paragraph of a given configuration option...

Oh, and apache + http/2 = huge TID sink.

I did a little bit of finger practice in Unity, nothing fancy just two spheres and a capsule-guy under Newtonian gravity and a force-driven player control script that works on spheres.

I will never understand how C# libraries spontaneously decide that some operations will be nonvirtual methods while others will be static methods. It is the exact same thing! You're just ruining intellisense for no conceivable reason!

Also, transform has a right but no left, the float return value of Vector3.SignedAngle is not the same unit as Transform.Rotate(Vector3, float), Transform has LookAt(Vector3 position) but not Look(Vector3 direction), to do that you need to

transform.rotation = Quaternion.LookRotation(Vector3.ProjectOnPlane(transform.up, direction).normalized(), direction)

you can't discover whether a collision encountered sticky or dynamic friction, you need to infer that by calling RigidBody.GetPointVelocity on both RB-s at the collision point, which has its own quirks. Apparently this is to keep the API engine agnostic, but any serious project will have its own physics materials which already specify the sticky friction coefficient. A simulation that works correctly with physics materials but doesn't discover the kind of friction as an intermediate result is not possible.

RigidBody's velocity isn't displayed in the GUI, so you can't give it an initial value without a dedicated script. I have a script on this fucking moon that does nothing but add a force in Start.

Is it just me or does Unity feel cheap somehow? Like a hastily written library for a research project that was never rounded out with the obvious features. I understand that it's a free product that catalyzed the golden age of indie game development, but I think it's seriously struggling to keep up, not with the showy investor bait stuff, but with the standard of comfort modern tooling provides.

In the kingdom of aws reigns the Owner of Products.
In his court many a vassal noble (or a "sre" as they are often called) delivers their tribute.
Wise ministers (called "analysts" in these here parts) advice the Owner of Products on how to instruct his sres and where to lead the kingdom.
Needless to say, in the court the blabber is endless and the egos of the courtiers, deservedly or not, are even larger.

But there is but one member of the court, leader of none but master of japes, who dares to mock not just the courtiers, but even the Owner of Products.
Tester the Jester, from the houses of Operations Research and Quality Assurance.

There is a unique relationship between a ruler and his jester. The jester mocks the ruler, with the most outlandish of propositions, with the most malicious interpretations of the ruler's orders, evidencing the most absurd (but mathematically viable) results of a plan.
The jester makes ridicule of the ruler's edicts... so that the Owner of Products may remain humble, without need to defer to any upstart courtier.
And, in a more subtile manner, the jester prevents any courtier from maliciously complying with the edicts of the ruler.
For all in the court have heard how the lowest among them voiced the preposterous interpretation... And dare not show themselves to be even lower.

TL;DR had an all-hands meeting of tech leaders with the allmighty PO. In the meeting there is this bloke who apparently spends all his time just fucking with the bigwigs' ideas. Dude is a department of one. It seems that his whole job is being an outlandish scenario simulator & sarcasm artist. I now have way more respect for this place.

research 10.09.2024

I successfully wrote a model verifier for xor. So now I know it is in fact working, and the thing is doing what was previously deemed impossible, calculating xor on a single hidden layer.

Also made it generalized, so I can verify it for any type of binary function.

The next step would be to see if I can either train for combinations of logical operators (or+xor, and+not, or+not, xor+and+..., etc) or chain the verifiers.

If I can it means I can train models that perform combinations of logical operations with only one hidden layer.

Also wrote a version that can sum a binary vector every time but I still have
to write a verification table for that.

If chaining verifiers or training a model to perform compound functions of multiple operations is possible, I want to see about writing models that can do neighborhood max pooling themselves in the hidden layer, or other nontrivial operations.

Lastly I need to adapt the algorithm to work with values other than binary, so that means divorcing the clamp function from the entire system. In fact I want to turn the clamp and activation into a type of bias, so a network
that can learn to do binary operations can also automatically learn to do non-binary functions as well.

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