MNPG Programming Language
A picture is worth a thousand words°
By Ian Tyner
Using additional measuring prototypes for gradual linkage plus maths.
This is where the real magic happens, with the right utensil, and the proper implementations.
And with the correct data, MNPG creates not just legal tech insurance, but also visual cues into the next barrier of solution based exercises and the criteria involved in the next best guess large language modelisations that serve multiplier audiences.
The truth of the matter is that with the plethora of programming languages available nowadays, and with the advancements of artificial intelligence, there is not that much attention span space ready to be used. You have to be the best in class, or the most popular in class. MNPG is neither; but a solution designed from image formats, as being the go to notion of utility.
MNPG isn’t just a form(at), but also a lingual model used for complex computations, in the cloud, live, and for non decoding purposes.
The crux of this language is as follows:
- created by an entrepreneur, for entrepreneurs usage
- uses Natural Language Processing (NLP)
- allows for AIP (Artifice Intelligent Property) integrations
- can be read once, and implemented over and over again
- simply put; MNPG is designed as using quotable blocks, in the beginnings of learnings
- creates a lingual first, rapid prototyping and MVP (minimum viable product) encasement
This language is different from others, as it adheres to imaging, and image based data recollection, as being the primary designative factors, involved, in fractal mathematics.
Why fractals?
Because when architectures are designed from the ground up, with little to no foreign interference, the math involved tends to end up looking like a fractal, if there is one way of design that is implemented.
Fractals lead to fractalisation, the procession involved in generating an organic footprint, that eventually zooms out, into a fractal shape, something that nonorganic programming does not recognise.
This procedural programme, defines the MNPG Programming Language.
With this said: enjoy programming with this code, as of far, it has worked tremendously, because of the attention to detail and the flexibility in the visual boundaries and forms that define this lingual application.
The main reason for this programming language to exist is for the continued interoperability of legacy image data, that can be translated to other operating systems beside OS MNPG, in a fast and stable fashion.
Citing authorship of each data point as a linkage, is key in addressing the continued use of such an image, as far as syntax is presented.
The main part of this language, like others, is that the data involved in this programme is crunched. This means that the smallest part of the information involved with computation, can be derived from itself.
It in itself, is a creationary topic; why focus on the image in the first place? Just like hypertext, images form the basis of virtualisation and redundancy.
There are other elements, like video, animation, and formatting, which can be argued as being equally important, however, it is an image, that provides the most bang for the buck.
And this is why deciphering the storage issue, is the number one issue that OS (Operating System) MNPG, solves. Sure there are other parts of the web, that can be argued as having better storage related advantages. But with an image, you can get access to the best of both worlds, the size product, dimensions product, and the visualisation product; and if you look close enough, you’ll also find that you can embed other datasets inside of an image, and with the right programme, you can decipher such an image as well.
This is where things get interesting, when you look at an image, as also being a program, and a container.
Why containerisation matters, is because again, we arrive at the interoperability issue, that is easily solved by imaging services.
MNPG Programming Language offers in its entirety, a framework, for better equipping the landscape of visualisations, into the virtualisation. The better this concept can be understood, the better the usage of the language can be comprehended.
The nature of it all, depends on deciphering an image into code, and that code into actionability. The further the code of an image can be decoded, the more levels of space storage can be included in this coding ladder.
Integrating architecture into the mainframe of programming is a whole other story. This is where image programming gets interesting.
The quest for knowledge is much like the thirst for information; the more you know, the more you want to know.
And with the proliferation of AI and AI agents, and all that encompasses artificial intelligence, creating with an intangible tangible, is ever more important.
Start with the realisations of programming, that clinches, a thought in the mind, and code in all realities.
Knowing when to implement an imagist type of coding, is where all the previously explained code attributes can be implemented into knowledge.
Then with more programming, the code gets intertwined with either text or visualisations that can be represented as text.
In the end, the user has to ask, what holds more value, a body of text, a line of text, an image, or the text that can be generated from an image?
It is with this conclusively clear argument, that working with images, above all, becomes a necessity for programmers and entrepreneurs, alike.
Now let’s get into the nitty gritty of MNPG Programming Language.
This language is different from others, as it is purely qualitative in substance and in core.
While qualitative, all of the elements serve an applied mathematics approach to problem solving.
For example, when you print something to the cloud, you simply execute: ‘launch cloud print’
And when you want to compile the code you’ve designed, all you have to say is: ‘execute programme’
But because this language is more on the natural language processing side of things, there isn’t a compiler, or that much code really.
All there is, is space governed by the user, to either name a file with code, or to embed code inside an image. These images can be compiled or not for further analysis. What this does is create an embedded programme, that can run along other programs; an image or a series of images can tell a programming story. And these images can be isolated designs, or they can operate in tandem with each other. What makes this process different, is that any image recognition software, preview application, or viewer, can act as a “compiler” in its own regard, solving another interoperability problem, with a quick and easy solution. The main thing, is that these image programs are cross platform compatible, more so, than designing with typical code, compilation, and execution, can be.
Whose to say that one language is better than another, as they all serve different purposes.
The MNPG Programming Language is clearly different, but at its core, is just a possible approach in getting more out of technology, and wasting less time in developing the learning foundations needed to get a workable prototype into provision and into the market sphere.
Now that you have a general idea of how this language works, here is some example code for quick and easy implementation.
- ‘implement crypto image asset’
- ‘create image portfolio’
- ‘encrypt images’
- ‘store image data in a container’
With a little bit of code, you can see that the applications for the MNPG Programming Language are simple to understand.
The complexity arrives when you start to untangle the data from an image; getting the big data, from a small data image, is an interesting easter egg for programming.
If you convert an image to text, you’ll find all the embedded image data that you need, sometimes this data translates to “assembly” level code, other times, you can think of this type of data as being an internal caption.
The more experimentations that you partake in, the better your understanding of this programming language.
There is not that much to it, when you consider that the visual, is more accessible, when you take heed to the fact, that a picture is worth a thousand words, or so the saying goes.
It is the interpretation of a picture that matters most. For it is the internalised meaning that is significant. This goes without saying, but with the correct analysis from a generality point of view, you’ll find that the value of the image increases.
This is how crypto mining of an image works. As a critique of the internal components of an image are gauged as being correct or not, as far as subjectiveness, the object value by the author of the image can be translated into a nonce reward.
Just another example of the many multiplier applications within the cryptography and blockchaining space.
The truth of the matter is most of these techniques and syntaxes, and protocols, of imageries, have already been applied; not in full, but piece by piece.
It is the coalescence of solidification where all of these practices equate to what can be considered a programming language by MNPG.
The origins of why MNPG focuses on the image format, as opposed to hyperlinks or plain text at the top of the analytical matrix, are simply put, that in the beginning, formats, like PNG and JPEG were easily translatable, for some of these use cases mentioned above.
This led to the image.mnpg format, and the official alignment from a publisher’s perspective, with the abundance of usable images and a new image format.
Then other realisations came to be, and the nuances of technology began to create the other criteria for a new language.
And thanks to MNPG’s establishment of being a foundation, the level solutions at stake, have been able to be revealed in due course.
Where do images go from here? Having just touched the surface of applications, there are other advancements which should not be undervalued, and in turn, here are some of the more complex image adaptations for the MNPG Programming Language.
The reality is that because of this approach to value hidden in plain sight, the research and analysis of imaging techniques isn’t that great, which is the main reason why this document was crafted, for education, as well as application purposes.
In its essence, anything that can be containerised, can be programmed, and anything that can be programmed, deserves its own programming language.
Now that technology’s adoption curve shifts regardless of class, anyone with a computer and the internet can enhance that tech curve.
For MNPG, this curve started out tough because of network wide intelligence deflectors and a large barrier to entry, for its author, Ian Tyner.
Over time, through self learning and education, Ian (me), was able to create advancements gradually, and then after going in on the space, on his own, made huge strides in acceleration on his own behalf. What once in some terms, took years, was able to be accomplished in a few days.
The same goes with this operating system (os.mnpg); the framework was created out of architecture, and then the architecting itself took about seven years, and then the crystallisation, at the end, took a few weeks.
What is important to note about this process, is that it is the beginning and the end which are the easiest, and the middle which takes the most time.
With the right environment, which is crucial, the process on both ends, and the mid work, can accelerate in due time, without any bottlenecks.
And for MNPG Programming Language, in the year of this content creation, the same can be said, that its putting the ideas down on paper, and hopping on the typewriter that is key in this procession.
Some people prefer to have their writing autogenerated, or created from an AI; but for this use case, and all the use cases by Ian Tyner, the writing is organic, and typed into reality with a keyboard, with his (my) very own fingers.
This distinction is important to note, time and time again, because of how many competitors there aren’t, in the organic creationary space, and how different this piece of work is, from the next AI “work” of creativity.
Now that the stage is set, you’re welcome to hop onto your own framework, to implement this programming language, and see if you like it.
The main difference between this framework and the next, is that its not really code centric, but direction aimed.
More and more thereafter, the user of such a language, can be pleasantly surprised, in what they can achieve, with MNPG, as it is one of the easier languages to learn, because of its intuitiveness.
Analyse an image, or create an asset with a camera, are both great places to start in the generative quest of creation.
Or you can use the commons, to acquire an image, and then decode it.
And if you’re lucky, someone can gift you a free asset image, and you can integrate into your own system.
There are many ways to begin, and believe me, if it sounds too good to be true, and that easy, in this case, it truly is.
The interesting thing about learning a new language, lingual or coded, is that the user’s entire perspective can become different in the sense that after learning it, they can communicate in a different way.
And what is even more interesting, is that other past learned languages by the user, can then be comprehended in a different way as well.
This cyclic learning rendition is where you have to be careful about which language to choose to learn and unlearn.
These types of precautionary tales are necessary for any language, computational or not.
For the MNPG Programming Language, because of the ease of use, and because the scale of learning is also very easy, compared to other programming languages, the conflicts mentioned above, don’t really apply, as they would be applicable in learning a pure code or foreign language from a different family of linguistics.