Microsoft came out with a new cloud suite of products.

They're naming it MS Up.

SCADA looks like something really interesting to learn.

Anyone familiar with it, what packages to get started with?

I was thinking of building a simulator api, maybe to flex and train what I'm learning in postgres, but I don't know where to begin yet. Seems like a big topic.

I know I haven't been responding to a lot of you lately. I've been busy helping neighbors and my community, doing MAAAAAATH, working on my car, and moving a shit ton of scrap and lumber.

I've been thinking about getting a motorcycle. Fuck, maybe I'm experiencing a midlife crisis, but early.

Been busy doing some design work as well for the game, and arrived at something that I'm satisfied with enough that I might demo it.

I'm also looking for a job, and I think I might give up programming as a career path and persue welding or trucking or something considering theres basically zero opportunities for it unless you went to college.

It's good to have hobbys anyway. And who wants to turn their hobby into a job right?

Anyway, thats whats been going on with me.

Completely unrelated, but heres a really fantastic introduction to the basics of type theory:

https://wscp.dev/posts/tech/...

PORTFOLIO INFLATION

when every junior is writing algorithms, the next step up, the only way to keep up is writing apps. When every junior is writing apps, the next leg up is writing an entire SN.

Eventually junior full stack devs are writing microservice streaming cloud backend content delivery optimized social networks wrapped in virtualization with load balancing, proper CI, public accessible analytics apis, written in custom webaseembly compiled scripting backend utilizing both the latest graphql and every single feature of postgres, while also being a web site builder, an in browser app, mobile optimized, designed to transmogrify your asset pipelines linearflow functional-oriented modular rust cratified turbencabulator while cooking your turducken with CPU cycles, diffusing your gpt, and finetunning your llama 69 trillion parameter AI model to jerk you off all at the same time.

And then the title "wizard" becomes a reality as the void of meaning in our lives occupied by the anxiety of trying to reduce the fear of rejection in job hunting, is subsumed by the brief accidental glance into the cthulian madness-inducing yawning abyss of the future which is all the rest of our lives we have to endure existing for until at last sweet sweet death consumes us and we go to annihilation never having to configure one more framework or devops deploy of another virtual environment.

And it dawns on us that we no longer develop or write code at all. No, everything has become a "service" in this new hellscape future. We slowly come to the realization that every job is really just Costco greeter, or eventually going to be reduced to something equivalent, all human creativity, free will and emotions now taken care of by the automation while we manage the human aspects, like sardines pushing against one another not realizing their doom has been sealed along with the airless can they have been packed into, to be suffocated by circumstance and a system designed to reduce everything to a competition of metrics designed by the devil, if the metrics were misery", and "torture", while we ourselves are driven by this ratfuck wheel to turn endlessly toward social cannibalism, like rats eating their babies, but for the amusement of wallstreet corporate welfare whores who couldnt turn a dime if it wasnt already stolen.

And on our gravestones, those immortal words are carved, by the last person who gave up the ghost, the last whose soul wasnt yey shovelled onto the coal fires driving the content machine consuming the world:

Welcome to costco. I love you.

!dev related

A friend was showing me pictures of British cooking. We were joking about it. But honestly it's so bad I legit almost threw up twice on her bedroom floor just looking at the images in google search.

We're getting done with SidTheITGuy's bachelor party where we auctioned him off.

Before it was through, the lucky winner who snagged him at the bachelor-auction had already sold him to another, gotham's most mysterious tech heiress, looking to do a mezzanine funding round on her relationship status:

Meet, Ms. Planky Le Planche, the new fiance of SidTheITGuy:

Who am I?

Some of you, because of the hyperbolic, outrageous, trollish, and often self-satirical nature of my posts, might doubt me. Thats completely relatable.

Heres the truth:
I was diagnosed in childhood with ADHD, fucking everyone, every male, these days is diagnosed with that. I was diagnosed bipolar. Hell anyone reading my posts could see that from a mile away. I was diagnosed on the borderline personality spectrum. Yeah, I could see that.

I was tested. They said I was in the 98th percentile for clerical ability, not extraordinary but pretty good, mathematical ability a little higher than that. My SAT was 1491. Not yale material, but I coulda been someone.

Over the years I studied a LOT of politics and read a metric fuckton of books. (40+ books over the course of three years).

I predicted every single presidential election since bush juniors second election. Three supreme court picks. Senatorial elections. Congresional elections. More than that.

I have a better analysis track record than some of the multidecade analysts sitting in the fucking NSA.

No I am not shitting you. No I am not exaggerating.

It's about the only claim to fame I get to legitimately make.

People ask me, "then why aren't you famous?"

How do you know I'm not.

Look I'm gonna tell you my actual name.
My real name is Lawrence B. Lindsey

Okay, I'm bullshitting for fun. But words I have written on alt twitter accounts have legitimately come out of presidential hopeful's mouths. No, this I am *not* bullshitting you about.

Imagine that. A guy who lived in his parents attic for five years, writing words that came out of presidential candidates mouths.

At one time I was about as popular and influential as that fuckboy catturd.

yes, really. No I am not fucking joking.

Under normal conditions I wouldn't talk about this or reveal it, because who the fuck cares? I'm just some dude on the internet, drunk, both on alcohol, and the pseudo-anonymous equivalent of bragging rights.

You know how many women I turned down because I could? You know how fucking drunk I am? They say a drunk man's words are a sober man's thoughts. Well, I'm not usually honest like this because the internet is full of false braggarts, and you tell people the truth and they don't fucking believe you.

I swear, it seems like I made some faustian bargain at some time, and can achieve no fame or lasting wealth in my life--to save my life.

Shit, I was talking to a chinese women who ran a bank in china (yes, really), who advised me to buy into bitcoin early on. Didn't have the money to. Woulda been a fucking millionaire if I did.
*Non-obvious* Ideas that major corporations are now persuing? Yeah those were sitting in my card index since the early 2000s.

I helped two people build and sell businesses. One for me tens of thousands. Another for millions. Yes, really. Got zero, and I mean, *zero* credit for it.

Point is, doesn't matter how famous you are, or coulda been, Doesn't matter the ideas you have, or had.

The world doesn't promote runners-up, or hasbeens, or wannabes, or could-bes.

What matters is execution.

If you're wandering through life, wondering when you're lucky break will be, stop. You have to realize, you make your own luck. Recognize the difference between what you can control, and what you can, and work on promoting your own ideas or business or values, instead of other people's dreams.

And for those wondering, yes I am drunk, and no, I ain't fucking kidding you in anything I wrote here.

The most important lesson I learned is this:
First work on your own success, before you work on the success of others.

p.s.

I give surprisingly good advice for someone who doesn't benchmark well on traditional measures of success. I know, even I was shocked when I looked at the statistics.

I finally got my github up.
You all can look at my terrible code, which is just glorified snippets. I don't mind.

Left out probably 98% of all the code larger than 10 lines because of jank, throwaways, and how poorly I documented it. Basically throwing shit on a wall.

I also left of the "maaaaaaths!" code because its already super convoluted and strictly a one-man thing. Likewise the web scrapers (barely documented and custom per site), and ML scripts.

https://github.com/YIntercept2

Did you know I once had an immediate rejection in the middle of a zoom interview, because the interviewer asked me "so whats your favorite browser", and I made a pretty obvious joke about using internet explorer.

That guy had no chill whatsoever. Fun times.

Established a new *much* tighter bound on the value of the D(10) dedekind.

Pasted code here:
https://pastebin.com/xYSND9NN

It's almost beyond doubt to be located somewhere between 10^75 and 10^77.

The last time jeeper posted at all was seven days ago. He said he was sick in his last comment.

I'm pretty sure hes dead jim.

Should we start planning the funeral?

Maybe I'm a complete beginner and don't know what I don't know, but having seen Terraform, I recognize immediately the value of simplifying deployment through configuration or 'infrastructure as code'.

I don't know a fucking thing about it, or how to actually do it, and don't even have a need for it because I don't program at that scale, but it looks really fun to work with.

It is the year 2451 ad and mankind rules the galaxy with a lazy iron fist. There are roughly 14,000 civilizations, comprised of just over
17,000 intelligent species on a quarter of a million earth-like
worlds. And all of them call themselves 'the galactic empire'.
No one told them that twenty planets doesn't qualify them for the title "galactic."

Well, we could rule, if we wanted to. Most of its just backwaters that no one wants anyway. It turned out that the reason no one invaded earth before was because they were too busy fighting themselves. Stupidity it appears, is not a unique human quality.That and the sex robots. Theres more of them in the galaxy than actual meatbags. Many species had taken to artificial wombs and 'vatbabies', which is exactly what they are called. Those poor bastards will carry that label for life.

We never did break light speed, but most of the rich exist in hypersleep anyway. Most of them only wake up once a year or so. There are some that only creek out of bed to check their stock portfolio. I hear there is even one trillionaire thats up and about once a century to ask if we have broken light speed yet.

Despite all the progress over the last 400 years, historians all agree about the most significant event in modern history.
The lobster went extinct two hundred years ago on earth.
Theres been riots ever since.

* * *

In other news I'm still working on the game I guess. It's like totally the most okay indie game you'll ever play--if I ever finish it.

I put about a year of work into the NPC system, and then chatGPT came out.

After everything thats happened, at this point I may just make a game about an indie dev making a survival game, being stuck in the actual apocalypse or some weird political dysopia.

Put it on rewind, it was originally a zombie game. But at the time the market got flooded and steam sales for zombie games cratered. So I pivoted to something more along the lines of fallout. Then the flash market crashed, bunch of publishers folded, and adobe stopped support for flash (probably for the best). Then newgrounds, which I was gonna launch on for promotion (because actual marketing is expensive), ended support for flash.
Was going the route of kickstarter, and that year the KS market got flooded and the bar rose almost over night so you needed super high production quality out the gate, and a network of support you already built for months.

We had a brief nuclear war scare, and I watched the articles come out about market saturation for post-apocalypse games, so I pivoted back to zombies. Then covid happened and the entire topic was really fucked. So I went back to fallout meets rimworld. Then we had a flood of games doing that exact premise pretty much out of the fucking blue, so I went for a more single-survivor type game. Then ukraine happened and the threat of nuclear war has been slowly sapping the genre of its steam, on well, steam.
Then I was told to get a cancer screening which I can't afford. Then I broke a tooth and spent a month in agony.

Then a family member died. Then I made no money from the sale of a business I did everything to help get off the ground, then I helped renovate an entire house on short notice and sell it, then I lost two months living in a hotel
while looking for a new place to live. Then I spent two and a half years suffering low-level alcoholism, insomnia, and drifting between jobs.
Then I wrote amazing poetry. And then I rediscovered my love of math. And then I made out for the first time in over a year. And then I rediscovered my love of piano and guitar. And then I fell into severe depression for the last year. Then I made actual discoveries in math. And I learned to love my hobbies again, and jog, and not drink so much, and sing, and go on long drives, and occasional hikes, and talk to people again, and even start designing games and UIs again. And then I learned that doing amazing things without a lot of money is still possible, and then I discovered the sunk cost fallacy, and run on sentences, and how inside me there was a part of me that refused to quit because of circumstances I couldn't control, and then I learned that life goes on even when others lives have ended, even when everything and everyone never had an once of faith in you, and you've become the avatar of the bad luck brian meme..still, life goes on.

And we try to pick up the pieces, try, one more time, because the climb, and the fall, and the getting back up, is all there is.

What I would recommend, if you're thinking of making a game, or becoming an independent game developer, is, unless you have a *lot* of money upfront (think 50-100k saved, minimum, like one years income *bare* minimum), and unless you already have a full decade in the industry--don't make a game.

Just don't.

!dev related

"Ah! Ah! Ah! AL QUEDA!!"

The opening theme song to the soon-to-be-a-hit docuromance-cum-comedy-on-ice, called "kidnapped-and-brainwashed in egypt: berry barrows story, starring samantha kaffir the isis headchopper, as herself."

Written by Adam sandler.

I wonder if the mods ever think I just write these posts to see the most unusual combination of tags possible.

There has to be orphan tags out there, tags associated with only a single post. Like half of them are probably because of me.

Though I demonstrated a hard upperbound on the D(10) dedekind in the link here (https://devrant.com/rants/8414096/...), a value of 1.067*(10^83), which agrees with and puts a bound on this guy's estimate (https://johndcook.com/blog/2023/...) of 3.253*10^82, I've done a little more work.

It's kind of convoluted, and involves sequences related to the following page (https://oeis.org/search/...) though I won't go into detail simply because the explaination is exhausting.

Despite the large upperbound, the dedekinds have some weirdness to them, and their growth is non-intuitive. After working through my results, I actually think D(10) will turn out to be much lower than both cook's estimate and my former upperbound, that it'll specifically be found among the values of..

1.239*(10^43)
2.8507*(10^46)
2.1106*(10^50)

If this turns out to be correct (some time before the year 2100, lol), I'll explain how I came to the conclusion then.

Anyone know what identity this is?

(a+b)/(1/(a-b)) == a^2 - b^2

This morning I was exploring dedekind numbers and decided to take it a little further.

Wrote a bunch of code and came up with an upperbound estimator for the dedekinds.

It's in python, so forgive me for that.

The bound starts low (x1.95) for D(4) and grows steadily from there, but from what I see it remains an upperbound throughout.

Leading me to an upperbound on D(10) of:
106703049056023475437882601027988757820103040109525947138938025501994616738352763576.33010981

Basics of the code are in the pastebin link below. I also imported the decimal module and set 'd = Decimal', and then did 'getcontext().prec=256' so python wouldn't covert any variable values into exponents due to overflow.

https://pastebin.com/2gjeebRu

The upperbound on D(9) is just a little shy of D(9)*10,000
which isn't bad all things considered.

Found a nifty way of generating the 7th dedekind number because of how it uses the difference of powers, and the sum of the fifth and sixth dedekind numbers:

((5**d(10))-(5^(9)))-((((5+168)*2)+7581)*2)

Pretty sure its a one-off though. Couldn't find any generalizations. Just a happy accident.

The next step for improving large language models (if not diffusion) is hot-encoding.

The idea is pretty straightforward:
Generate many prompts, or take many prompts as a training and validation set. Do partial inference, and find the intersection of best overall performance with least computation.

Then save the state of the network during partial inference, and use that for all subsequent inferences. Sort of like LoRa, but for inference, instead of fine-tuning.

Inference, after-all, is what matters. And there has to be some subset of prompt-based initializations of a network, that perform, regardless of the prompt, (generally) as well as a full inference step.

Likewise with diffusion, there likely exists some priors (based on the training data) that speed up reconstruction or lower the network loss, allowing us to substitute a 'snapshot' that has the correct distribution, without necessarily performing a full generation.

Another idea I had was 'semantic centering' instead of regional image labelling. The idea is to find some patch of an object within an image, and ask, for all such patches that belong to an object, what best describes the object? if it were a dog, what patch of the image is "most dog-like" etc. I could see it as being much closer to how the human brain quickly identifies objects by short-cuts. The size of such patches could be adjusted to minimize the cross-entropy of classification relative to the tested size of each patch (pixel-sized patches for example might lead to too high a training loss). Of course it might allow us to do a scattershot 'at a glance' type lookup of potential image contents, even if you get multiple categories for a single pixel, it greatly narrows the total span of categories you need to do subsequent searches for.

In other news I'm starting a new ML blackbook for various ideas. Old one is mostly outdated now, and I think I scanned it (and since buried it somewhere amongst my ten thousand other files like a digital hoarder) and lost it.

I have some other 'low-hanging fruit' type ideas for improving existing and emerging models but I'll save those for another time.

This proof of the collatz conjecture looks fun;
https://drive.google.com/file/d/...

Every sufficiently advanced ui kit is indistinguishable from a half-assed html5 browser.

I think styling languages were the mistake of all time. And that we should go back to artists implementing themes on top of 9slice technology.

Fight me.

I wonder if anyone has considered building a large language model, trained on consuming and generating token sequences that are themselves the actual weights or matrix values of other large language models?

Run Lora to tune it to find and generate plausible subgraphs for specific tasks (an optimal search for weights that are most likely to be initialized by chance to ideal values, i.e. the winning lottery ticket hypothesis).

The entire thing could even be used to prune existing LLM weights, in a generative-adversarial model.

Shit, theres enough embedding and weight data to train a Meta-LLM from scratch at this point.
The sum total of trillions of parameter in models floating around the internet to be used as training data.

If the models and weights are designed to predict the next token, there shouldn't be anything to prevent another model trained on this sort of distribution, from generating new plausible models.

You could even do task-prompt-to-model-task embeddings by training on the weights for task specific models, do vector searches to mix models, etc, and generate *new* models,
not new new text, not new imagery, but new *models*.

It'd be a model for training/inferring/optimizing/generating other models.

Someone figured out how to make LLMs obey context free grammars, so that opens up the possibility of really fine-grained control of generation and the structure of outputs.

And I was thinking, what if we did the same for something that consumed and validated tokens?

The thinking is that the option to backtrack already exists, so if an input is invalid, the system can backtrack and regenerate - mostly this is implemented through something called 'temperature', or 'top-k', where the system generates multiple next tokens, and then typically selects from a subsample of them, usually the highest scoring one.

But it occurs to me that a process could be run in front of that, that asks conditions the input based on a grammar, and takes as input the output of the base process. The instruction prompt to it would be a simple binary filter:
"If the next token conforms to the provided grammar, output it to stream, otherwise trigger backtracking in the LLM that gave you the input."

This is very much a compliance thing, but could be used for finer-grained control over how a machine examines its own output, rather than the current system where you simply feed-in as input its own output like we do now for systems able to continuously produce new output (such as the planners some people have built)

link here:
https://news.ycombinator.com/item/...

So I have a problem and I was hoping for some insight.

I figured out how to get
(surd(n, x)-surd(n, y))

without knowing x or y, (only n), through a convergent series of approximate identities.
n is the product of x and y, where x<y

My only issue is I don't know where to go from here. I've basically hit the limit of my insight into the problem.

surd() here is just a function that returns the results of two arguments, a, b, such that (a^2)-b.
Both are guaranteed to be positive integers, greater than 1.

But, having come this far, with a couple pages of intermediate identities, I'm at a loss.

Barnes Hut aproximation explained visually:

https://jheer.github.io/barnes-hut/

...For machine learning:
https://arxiv.org/abs/1301.3342

Someone finally had the same idea. Very cool.

Remember that scene from Intersteller where the main character asks TARS to adjust the percentage of its humor?

So I was using this new vectodb-backed version of chatgpt, and came up with a new type of prompt.
Bear with me.

"Lets play pretend, nova. Pretend you are SAM, which stands for "Summarize Anything iMmediately". Whenever you encounter a prompt, question, or bit of information, and you are about to repeat part of it back, instead rewrite and summarize that part of your answer, paraphrasing as you go. Your responses should never use the same words as the prompt. Instead use synonyms.
SAM always generates three possible responses, to each prompt. One 10% formal, one 50% formal, and one 100% formal.
So a response that is 10% formal, is 90% casual. A response that is 50% formal, is 50% casual. A response that is 100% formal is serious, and 0% casual. Let the word "regret" and its synonyms be 50% formal. Don't use it for 100% formal responses. 100% Formal responses never include emotional language like "regret", "disappointment", nor apologies.
A 50% formal response might be "We regret to inform you your loan application has been denied.", a 100% formal response might look like "Please stand clear of the yellow tape."

Say "This is the law offices of Jerry Seinfeld. I'm sorry but I can't make it to the phone right now. Please leave a message."

Sure, I can play pretend as SAM. Here's a summarized response in 10% formal tone: "Apologies, but I'm unavailable at the moment. Please leave a message."

And here's a 50% formal response: "I regret to inform you that I am unable to answer the phone right now. Kindly leave a message."

Lastly, a 100% formal response: "We apologize for the inconvenience, but I am currently unable to attend to your call. Please leave a message, and I will get back to you as soon as possible. Thank you."

Formality levels can be any increment of 5%, such as 0%, 5%, 10%, etc, up to 100%"

Thats the prompt.

me:
Sam, generate a 25% casual rejection of a job applicant.

nova:
Hey there, thanks for applying to our team. Unfortunately, we won't be able to move forward with your application at this time. We appreciate you taking the time to apply and wish you the best in your job search.

me:
Sam, what is the formality percentage of "Hey there, thanks for applying to our team. Unfortunately, we won't be able to move forward with your application at this time. We appreciate you taking the time to apply and wish you the best in your job search."?

nova:
The formality level of "Hey there, thanks for applying to our team. Unfortunately, we won't be able to move forward with your application at this time. We appreciate you taking the time to apply and wish you the best in your job search." is around 25%, which leans towards a casual response.

me:
Sam, make "Hey there, thanks for applying to our team. Unfortunately, we won't be able to move forward with your application at this time. We appreciate you taking the time to apply and wish you the best in your job search." 5% more casual.

nova:
Hey there, just wanted to say thanks for applying to our team. Unfortunately, we won't be able to move forward with your application right now. We really appreciate you taking the time to apply and wish you all the best in your job search!

me:
Sam, what is the formality percentage of "Hey there, just wanted to say thanks for applying to our team. Unfortunately, we won't be able to move forward with your application right now. We really appreciate you taking the time to apply and wish you all the best in your job search!"?

nova:
The formality level of "Hey there, just wanted to say thanks for applying to our team. Unfortunately, we won't be able to move forward with your application right now. We really appreciate you taking the time to apply and wish you all the best in your job search!" is around 20%, which leans towards a more casual tone.

* * * * *

You can actually go back and see it.

Heres the original output, 25% formality:
"Hey there, thanks for applying to our team. Unfortunately, we won't be able to move forward with your application at this time. We appreciate you taking the time to apply and wish you the best in your job search."

Heres the one thats 5% more casual:
"Hey there, just wanted to say thanks for applying to our team. Unfortunately, we won't be able to move forward with your application right now. We really appreciate you taking the time to apply and wish you all the best in your job search!"

If you look at the differences, the output version adds five words, subtracts three words, for a total of two words gained.

The original sentence has 39 words.

2/39 = 0.05

The sentence length actually grew an equal percentage to the informalness.
It grew linearly to the difference of the length of the more casual version
versus the more formal version, divided by the length of the original.

After learning a bit about alife I was able to write
another one. It took some false starts
to understand the problem, but afterward I was able to refactor the problem into a sort of alife that measured and carefully tweaked various variables in the simulator, as the algorithm
explored the paramater space. After a few hours of letting the thing run, it successfully returned a remainder of zero on 41.4% of semiprimes tested.

This is the bad boy right here:
tracks[14]
[15, 2731, 52, 144, 41.4]

As they say, "he ain't there yet, but he got the spirit."

A 'track' here is just a collection of critical values and a fitness score that was found given a few million runs. These variables are used as input to a factoring algorithm, attempting to factor
any number you give it. These parameters tune or configure the algorithm to try slightly different things. After some trial runs, the results are stored in the last entry in the list, and the whole process is repeated with slightly different numbers, ones that have been modified
and mutated so we can explore the space of possible parameters.

Naturally this is a bit of a hodgepodge, but the critical thing is that for each configuration of numbers representing a track (and its results), I chose the lowest fitness of three runs.
Meaning hypothetically theres room for improvement with a tweak of the core algorithm, or even modifications or mutations to the
track variables. I have no clue if this scales up to very large semiprime products, so that would be one of the next steps to test.

Fitness also doesn't account for return speed. Some of these may have a lower overall fitness, but might in fact have a lower basis
(the value of 'i' that needs to be found in order for the algorithm to return rem%a == 0) for correctly factoring a semiprime.

The key thing here is that because all the entries generated here are dependent on in an outer loop that specifies [i] must never be greater than a/4 (for whatever the lowest factor generated in this run is), we can potentially push down the value of i further with some modification.

The entire exercise took 2.1735 billion iterations (3-4 hours, wasn't paying attention) to find this particular configuration of variables for the current algorithm, but as before, I suspect I can probably push the fitness value (percentage of semiprimes covered) higher, either with a few
additional parameters, or a modification of the algorithm itself (with a necessary rerun to find another track of equivalent or greater fitness).

I'm starting to bump up to the limit of my resources, I keep hitting the ceiling in my RAD-style write->test->repeat development loop.
I'm primarily using the limited number of identities I know, my gut intuition, combine with looking at the numbers themselves, to deduce relationships as I improve these and other algorithms, instead of relying strictly on memorizing identities like most mathematicians do.
I'm thinking if I want to keep that rapid write->eval loop I'm gonna have to upgrade, or go to a server environment to keep things snappy.

I did find that "jiggling" the parameters after each trial helped to explore the parameter
space better, so I wrote some methods to do just that. But what I wouldn't mind doing
is taking this a bit of a step further, and writing some code to optimize the variables
of the jiggle method itself, by automating the observation of real-time track fitness,
and discarding those changes that lead to the system tending to find tracks with lower fitness.

I'd also like to break up the entire regime into a training vs test set, but for now
the results are pretty promising.

I knew if I kept researching I'd likely find extensions like this. Of course tested on
billions of semiprimes, instead of simply millions, or tested on very large semiprimes, the
effect might disappear, though the more i've tested, and the larger the numbers I've given it,
the more the effect has become prevalent.

Hitko suggested in the earlier thread, based on a simplification, that the original algorithm
was a tautology, but something told me for a change that I got one correct. Without that initial challenge I might have chalked this up to another false start instead of pushing through and making further breakthroughs.

I'd also like to thank all those who followed along, helped, or cheered on the madness:
In no particular order ,demolishun, scor, root, iiii, karlisk, netikras, fast-nop, hazarth, chonky-quiche, Midnight-shcode, nanobot, c0d4, jilano, kescherrant, electrineer, nomad,
vintprox, sariel, lensflare, jeeper.

The original write up for the ideas behind the concept can be found at:
https://devrant.com/rants/7650612/...

If I left your name out, you better speak up, theres only so many invitations to the orgy.
Firecode already says we're past max capacity!

Officially faster bruteforcing:

https://pastebin.com/uBFwkwTj

Provided toy values for others to try. Haven't tested if it works with cryptographic secure prime pairs (gcf(p, q) == 1)

It's a 50% reduction in time to bruteforce a semiprime. But I also have some inroads to a/30.

It's not "broke prime factorization for good!" levels of fast, but its still pretty nifty.

Could use decimal support with higher precision so I don't cause massive overflows on larger numbers, but this is just a demonstration after all.

If hiring managers really want to hire based on skill, what they should be doing is testing for one thing:

The ability to take a specification, written in general language, notice deficiency, communicate with the 'client' (manager) to hash out what needs done, and the (explicit) ability to read documentation on libraries or tooling outside the dev's core skillset.

If a dev can read a spec, talk to a client to work out whats lacking, and then identify what they need to know and where to find it, thats 90% of the skills they need from what I can see.

tl;dr version of it, is they should be explicit about the requirements for reading/implementing specs and finding the correct documentation.
Something along the lines of
"can you form your letters? Are you able to follow instructions on the back of a cake box? Then there may be a position waiting for you!"

For rating rants:
count the number of fucks/curses and find the rank of a given rant!

0-5: GENTLE AS A LAMB
6-10: ANGRY GOAT
11-15: NUN WITH PMS
16-20: RUSH HOUR TRAFFIC
21-25: CANTANKEROUS VIETNAM VET
26-30: BREAKING SHIT
31-35: DOMESTIC DISTURBANCE
36-40: BIPOLAR EPISODE
41-45: DESPAIR EVENT HORIZON
46-50: BROKEN CAPSLOCK
51-55: WOKE UP AND CHOSE VIOLENT
56-60: MID LIFE CRISIS
61-65:MASTURBATING WITH WORDS
66+ : MASTER GRAND WIZARD

Never forget, learning feels like flailing.

If you're not flailing, you're not learning.

Warning: does not apply to swimming.