Lynsei Brand Architecutre Definitions Registry
These are all very common terms in Lynsei Brand Architecture, which can include all sorts of references to AI/ ML, Socio Technical Theory, Decision Making, Distributed Systems, and other types of problem solving methods, behavior-driven systems and methods, and resolution techniques for project and people management, assuaging, and to ensure the competency of a team or project.
| Term | Definition |
|---|---|
| 2022 Rollout Plans | "Rollout" is a strong word. Perhaps it is best said that I wish to "Integrate" with other depts. pending James' final approval. More on the plans here. |
| Isomorphic Design | Like this. It follows the Single Responsiblity Principle of 12 factor apps, and produces really clean code that is easy to read and short to type. It fits in as one of the underlying priniples of immutable IOWA architecture (Insert Only Write Archive). I sort of came up with that, I think I may be the only person who uses it. But I'm so fond of it I actually purchased insertonly.com so I can name my servers something like deletearchived.archivedeleted.nevermodify.neverdelete.insertonly.com. I know it is silly, preposterous even. ;) |
| L7NS | Lyns Language at github.com/lynslang the bootstrap of which is at github.com/runtimelibs/bootstrap |
| DBSP | Data Bridging System Pointers provide a method for dependency resolution. See this story. |
| DSSC | Deep Stream Server/ Client Pattern with API Gateway. This is an Architecture pattern described in the Idiomatic Table of the Paradigms (a play on words, the periodic table of the elements). A link to that diagram can be found here. |
| Minted | See this story. Minted loaders provide an encryption vehicle, and a way to run Docker without actually running Docker. It does this in a cross-platform way (Linux, Windows, Mac) |
| BIG | Baseline Integrations for Graph |
| UPEND | Upend Apps are a disruptive industry technololgy, and is simply an Acronym for: Universal Platform of Enterprise NoDocker/Node+Docker Apps |
| Forward Declarations | Place holders for future functionality that does not yet exist, but will. It's only by the benefit of the doubt that a person will believe that it would or could exist in the future. When it does exist, these simply turn into definitions automatically in all documentation. Example: The developers who wrote Docker had to imagine a fake object that creates a runtime instance of itself (like what?). At build time the "image" is always immutable because they use Union File System which is a layered filesystem. These were almost certainly defined in some form of documentation as forward declarations long before the code was written. These sorts of things need to be planned out and thoroughly documents, elaborated, contemplated upon, etc. The only way to do that is ... Forward Declarations. You will see these all over my architecture. I have many names and references to things in flight, or which do not exist yet. It helps me remember what they are. |
| Morphological Invocations | Word fragments morphed and used to call or refer, or alias other programs/terminology. |
| Assessibility of Assythment | A Scotch legal term used to describe fourteenth Scottish law in away that refers to indemnification for loss that is serious, even though the usage of this term is more than likely sarcastic or inflammatory and not necessarily intended to distrurb or mock the dead. The target of this reference is actually obsolescent software code, and so the discussion subject is now an entirely obsolete remedy that has been replaced by a modern system (like paying debts for killing your family member in a criminal act in Scotland in the Fourteenth century). Therefore the reference is used when code or programs that are so old, or where the level of obsolescence is so high that the encounter makes the writer feels compelled to make a fourteenth century Scottish reference to drive home their antagonistic agenda. The whole thing is quite silly really, and this in-depth explanation is pretty unnecessary. |
| Demonstratability | How easy would the thing be to demo? |
| Adaptable Reporting | Reports that you can configure in the UIX, like the ones we have in our Primary UI that use Cube.js to manage analyics dimensions as an abstraction layer, specifically geared for reporting and using a database that is synced less frequently than primary read/write serverless caches are. |
| Exploratory-Depth Sensor | A sensor program to determine if a topic should be explored further in terms of its algorithm. Sometimes you just can't know if something is going to work because it is too difficult to predict how it will respond through emulation or test harnesses alone. |
| Fulfilment as Outcome | The delivery payload of a bot response. |
| Purpose and Spirit | The definitive and underlying, sometimes not obvious intention, of an action for a bot to perform/solve. |
| Condition as Attribute | All Attributes are conditions in the Matriarch API, and all Requirements are mapped the same way as references/attributes. All fields can report whichever metadata they are mapped to by referencing a sync table that aggregates the metadata as flat references instead of foreign keys. These tables can be found in L7_aggregates, and this action is typically performed out of band using resources that are distinctly separate from backup systems, metrics, or primary delivery mechanisms. |
| Conditional Reference | A referencec that is predicated in the database by a condition. These are normally handled with Finite State Machine References that link to Routines through 1bit (8 byte) references. Each 8 bit reference is mapped in the machine reference data store to a key library that is portable, permutated algorithmically, and migrates resiliently using web3 technologies such as IC/IPFS. |
| Appropriating Requirement | A requirement which when satisfied absorbs a different process, record, or room/space. This can be used in absorption of responsiblities when using CI systems that handle blue/green deployments, and it can be used in scenarios such as where a bot takes over a responsibility, such as monitoring a room that has been pulled through the Rust SDK for Matrix API, or Open Horizon. |
| Fatalistic Requirement | A requirement delivered as a promise or state machine outcome, which is satisfied only when destroying the object from which it was requested, or the target of its payload. |
| Tokenized Lexical Semantics | The advantage and artform knowing exactly when to use Semantics that include the derivation of extremely complex scenarios from taking a very short item of code and turning it into tokens.F1 It might not be immediately obvious, but the capability to write four language based key/value pairs with such a short syntax sugar has many advantages, especially in conversational ML scenarios that might be used for Natural Language Processing Text to Speech conversion, or Speech to text interpretation. The many reasons fall beyond the scope of this article, but these sorts of scenarios is why we prefer a markup such as BotML over Microsofts Botkit Rules Engine Interfaces in Typescript. Instead we prefer BotML with a plugin that converts some of the semantics to Tokenized Lexical analysis operations. This makes the demonstrabillity much greater, the conversation in general is far easier to understand, reader comprehension goes up, and it's easier to fit those semantics into tight spaces such as SMS messages. |
| Finite State Machines | Finite State Machines are used to describe a process with a set of aliases and UML that must be executed in a certain order. We use asynchronous state machines so that processes that can be parallelized are, but those that need to wait before proceeding must catch up after a promise resolves. |
| BotML Graphs | Rules Engines with complex logic bound to nodes, which are Finite State Machines (FSM) and that have conditional logic UML paths which are declaratively described and executed via Promises. This affords vast avenues for ultra-complex graph-root style answers to questions based on conditions, motivations, feelings, and which will ultimately lead to outcomes/ Fulfillments (the payload delivered by the conversational NLP bot) |
| All-encompasingly | Embracing Diversity and Inclusion, and respect the lifestyles and values of other people even if we might not understand them. A big part of it is providing your fellow human "the benefit" of "the doubt". This basically means, that while in security-lingo that our systems are "Zero Trust" or "Trustless" by default not trusting any access to any resource, as human beings we must do the opposite in daily life. Most people have good intentions, and so it is always important to assume and provide an opportunity to everyone to grow the level of trust they have with you through providing them the benefit of your implicit trust. For us humans trust is one of those things that is provided free, and is provided implicitly but it can also be broken in an instant, and once it is gone it can be the most difficult thing in the world to regain. |
| Angle infinitum | This refers to imagining all the angles of a problem or, in reference to Diversity and inclusion this term is directly corrleated to "the road to hell is paved with good intentions". As an example: Many organizations attempt to embrace diversity by forcing individuals or groups to connect and conversate, assuming that this practice embodies the characteristics. In part, they are right. It does embody the characteristics of inclusion because you are attempting to invite afford avenues for collaboration even if the office setting is remote. Part of this is also a fallacy however, because embracing diversity means embracing those who are different from us and their values. Most people are extroverts, but for every five people who are extroverted, one or two might be the opposite. By forcing interactions that make some people uncomfortable you unfortunately still are not accomodating the feelings of everyone but instead are just simply catering to the requirements of some. For D&I its always best to imagine what might some feel perfectly comortable, might make others resistant or hesitant, or even uncomfortable. |
| Bio-informatics | The science of collecting and analyzing complex biological data such as genetic codes. |
| COMPRESS | Compose Orchestrations Managing PM2 Replicating Event-driven Serverless Software. Orchestration can be programmatically achieved, and even scheduled by PM2 using docker-compose and our process scheduler, built right into ModInterop.com |
| SPREAD | Single Process (PM2) Realtime Event Architecture Delegation. The opposite of docker-compose, these are single processes within a child container that can be controlled by specifying the JSON for PM2 proccess management within that container at launch time. They can be scheduled the same exact way. |
| Term | Definition |
|---|---|
| Informal Fallacy | that relates to a language-related defect in an argument. |
| Linguistic Fallacies | Potentially created using words or sentences that have vague, unclear, or multiple meanings or other inconsistencies. Such fallacies are also referred to as fallacies of ambiguity or verbal fallacies. |
| Term | Definition |
|---|---|
| Equivocation | One of the six linguistic fallacies listed by Aristotle. In English (as well as other languages), many words have multiple meanings. One extreme example is the word "cleave", which has two opposite meanings: to adhere and to separate. An argument that uses one meaning of a word in one part of the argument and another meaning of the word in another part commits the fallacy of equivocation.2 |
| Amphiboly | Occurs when premises in an argument are amphibolous, which means that they are ambiguous because of careless or ungrammatical phrasing. While such ambiguous sentences occur relatively frequently, they don't occur too often in arguments, and hence the fallacy of amphiboly is quite rare. "Surely you insist on being what you insist on being. You insist on a stone being: therefore, you insist on being a stone." In this argument, the phrase "you insist on being" is interpreted both as "you assert the existence of," and "you assert that you are." It isn't likely to fool too many people, though. (Aristotle) |
| Accent | Inflections are indicated by accents including misplaced emphasis |
| Composition | This fallacy involves arguing that a property shared by all members of a set must apply to that set, or that a property shared by all parts of something must apply to the whole. For example: "All of the players on the team are good. Therefore the team must be good." Used to describe a player, the word "good" relates to the player's athletic skills, while when used to describe the team, it relates to their ability to win games. |
| Division | Another fallacy described by Aristotle. The fallacy of division is the opposite of the fallacy of composition. In other words, it involves concluding that a property of something must apply to all its parts, or that a property of a set must apply to all members of that set. For example: "The team is good. Therefore all the players must be good." |
| Form of Expression or Figure of Speech | This is the last of Aristotle's six Fallacies dependent on Language. It involves being misled by the structure or etymology of a word. "The only proof capable of being given that an object is visible, is that people actually see it. The only proof that a sound is audible, is that people hear it: and so of the other sources of our experience. In like manner, I apprehend, the sole evidence it is possible to produce that anything is desirable, is that people do actually desire it." ... here the reader is misled by the termination of the words; to say that something is visible is to say that people can see it, while to say that something is desirable is to say that it is worthy of desire. |
| Misplaced Emphasis | Changing the emphasis on syllables or words of an English sentence can change the meaning of the sentence or suggest a different meaning. For example, compare what is meant by "We've never caught Debbie stealing anything." versus "We've never caught Debbie stealing anything." |
| Abstraction or Quoting Out of Context | This fallacy involves removing a passage from its surrounding context, and possibly excerpting it or altering emphasis, so that the meaning of the quotation becomes different from what the original author intended. |
| Argument from Innuendo | Innuendo is a veiled attack on character or reputation. For example, a dean of students, asked whether a graduate had any disciplinary problems, replied "No, we were never able to convict him of any violations of college rules." The dean implicitly suggests that the university had suspected and/or investigated disciplinary problems. |
| Term | Definition |
|---|---|
| Innate Constructivism | Constructivism is ‘an approach to learning that holds that people actively construct or make their own knowledge and that reality is determined by the experiences of the learner. If you have made your own way in the world rather than learning from an Institution, you are quite possibly an implicit representative of this set of learning idealogy. |
| Incubated Constructivist Reflection | Constructivist teaching allows the student to build their knowledge through questioning, and through experience. ... They must experience it to understand it. This paired with the experience-driven belief system that conccepts take a great deal of time to incubate and reflect on in order to be fully learned, and that in doing so it might take a great deal of time to reflect, write, and think deeply on a topic before any sort of decisive action can be taken to appropriately address it |
| Term | Definition |
|---|---|
| Gold Plating | I'm a firm believer that people who reference "gold plating" in the context that someone was too creative or took too long on a project, do not empathize or understand the creative process and shouldn't be involved with creative teams. No everyone feels that way, matter of fact it can be an unpopular opinion at times. As you probably already guessed, my personality is not "people pleasing" focused. So I'll more often represent that I actually admonish gold plating because it embellishes and expands the creative process, and I will never stop creativity from happening, even if it messes with the timeline. On teams where timeline is critical, I find that this is often a major detriment, but in Research and Development groups it is almost certainly my greatest asset and strength. |
| Resulting Predictivity | Organizations are turning to predictive analytics to help solve difficult problems and uncover new opportunities. Common uses include: |
| 1. Detecting fraud. Combining multiple analytics methods | |
| 2. Optimizing marketing campaigns. Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. | |
| 3. Improving operations. Many companies use predictive models to forecast inventory and manage resources. | |
| 4. Reducing risk. Scoring, such as credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. | |
| Logarithmics | logarithm, the exponent or power to which a base must be raised to yield a given number. Expressed mathematically, x is the logarithm of n to the base b if bx = n, in which case one writes x = logb n. For example, 23 = 8; therefore, 3 is the logarithm of 8 to base 2, or 3 = log2 8. |
| Term | Definition |
|---|---|
| Waterfall | Waterfall is not a dirty word. In fact, if done correctly it is the fastest type of development that can be performed, especially in small team or by an Individual. My development methods embrace Waterfall and Gold Plating, and I waste absolutely zero time on refactoring, and I do it near constantly. For example, I just refactored the entire database schema for Matria in the past few days because I've added new requirements and it inherently created obsolesence in the data model which necessitated further abstraction. I write absolutely no code except for independent re-usable components that are either built into my Language, which can be re-used throughout all systems, or as a container or package/ collection. These components can also be referenced and re-used. All business logic is always performed inside a JSON based rules engine so that rules may be referenced and utilized again and again. All rules are not directly referenced either, the are used only by Finite State Machine Code which provides a UML path, logical architecture definition, and a needed abstraction in the event the rules change, they know how to process such information. For instance, all rules have a fallback rule. In the event a rule is deleted or modified in the schema, which would inherently impact the code, the FSM would look up the next logical rule in the set by virtue of a built-in, contextually sensitive tagging mechanism. This afford the system the ability to have many changes without harming any active rules. That being said, all data in my distributed systems is 100% immutable. That means it follows IOWA (Insert Only, Write, Archive). No data is ever deleted or modified. If a record is archived it is not "Deleted" but instead marked as archived and this allows a sub-system to move the data outside the primary database so it can still be referenced as an archived rule but it will not impact system performance long-term. |
| Context Sensitive | This means your AI system is able to take what it learned from one context and apply it to another to perform better on a similar task. For example, a contextual AI in charge of transcribing a company meeting could instantly recognize and link a project name that was once mentioned in a different meeting. |
| Inference Emulation | As it learns from each interaction, your AI system gets better at considering every aspect of a situation to deduce what the end-user truly needs at that moment. For example, a self-driving car could capture environmental cues, like wet roads and pedestrians ahead, then automatically reduce its speed. |
| SocioTechnical | in organizational development this is a term that recognizes the interaction between people and technology in workplace systems. The term also refers to the interaction between society's complex infrastructures and human behaviour. In this sense, society itself, and most of its substructures, are complex sociotechnical systems. |
| Decision Shading | |
| Feature/Brain Storm | |
| Easier to ask Forgivness | |
| Look before you leap | |
| Invalid assumptions | More often than not, invalid assumptions are the root of all problems in decision making. There has not been a clear understanding and specification of intended behavior. The fixtures have been constructed based on assumptions which simply did not hold under real production conditions. Or perhaps the customer provided innaccurate requirements. For example, it was assumed that the input data to a system would be of higher quality than was actually the case, which caused many unexpected failures in production. Machine learning models commonly encounter this issue, as it takes large quantities of real-world data that is highly refined in order for analysis and development of neural networks to occur and actually produce the results you are looking for. To ensure accuracy, L7ns uses a suite of tools and brings to the forefront the vast quantity of ML algorithms and technologies in a way that both consolidates and simplifies them. This is all predicated on the customizability that is intrinsic to the language. All concerns related to real-time data processing, resiliency, serverless, edge, and other such technologies are tangential to the project with some of the alorithmics actually being the last on the list for considerations or concerns. With invalid data comes (often times) invalid assumptions, and this is at the heart of the program what we are trying to prevent from happening by augmenting Fish with customizability features, and tailoring the software systems and registries for fine-tuned ModelOps. |
Figure I.
>>> import esprima
>>> program = 'const answer = 42'
>>> esprima.tokenize(program)
TOKENIZER_OUTPUT
[{
type: "Keyword",
value: "const"
}, {
type: "Identifier",
value: "answer"
}, {
type: "Punctuator",
value: "="
}, {
type: "Numeric",
value: "42"
}]
The display and usability of data is pertinent not only to linguistics and informatics, but pretty much to all the fields including many of the medical fields we operate in at L7.
In that vein I've been creating language enhancement programs that all operate on the framework of CLIs, NPM packages, cryptography software, and other assets that the L7NS program has from day one.
The intention here is to minimize this sort of work for others, since it's taken me months to compile and sift through. Ultimately, how people use or invoke L7ns is truly their own choice,
as the entire purpose of a DXP is definitely empowerment of developers. I am attempting to provide the ulimate array of tools, and the most secure and reliable environment with which to work.
- Teaching people logic teaches them how to argue. Because people already argue too much, there is no need to teach logic.
- If someone breaks the law, they are acting irresponsibly. Therefore, anyone who breaks the law is not responsible for his actions; therefore, that person's actions aren't their fault.
- Scientific authorities state that smoking causes cancer, but I know a lot of scientists who can't even control their own kids very well, so what kind of authorities could they be?
For some reason I retain a very long list of Decision making methods for various sorts of scenarios. They come in handy sometimes.