Every transaction processed by Tx-Rails generates two separate data structures under different privacy regimes: a **Transaction Metadata Trace**, and the **content**.
The Transaction Metadata Trace
The Trace is an immutable, 33-field record that travels with every transaction. It is a **design-frozen contract** — the schema does not vary by customer, deployment, or integration mode, so any technical reviewer can audit exactly what the platform sees.
The 33 fields fall into five categories:
Cryptographic commitments** (3 fields) — Merkle leaf hash, transaction signature, block Merkle root. Pure cryptographic objects; no business meaning.
BFT consensus metadata** (3 fields) — block ID, slab ID, block height. Where the transaction lives in the ledger; reveals nothing about content.
Opaque identity tokens** (20 fields) — federationID, counterFederationID, sector, counterSector, pyramid, counterPyramid, dataCentre, counterDataCentre, country, counterCountry, database schema reference, user ID, role ID, and others. All carried as anonymised byte arrays. A user ID is `U-7f3a92`, not `Jane Smith`; a table reference is the schema slot, not its business meaning. The mapping from token to recognisable identity stays on your side.
Operation discriminators** (4 fields) — request type and operation codes that identify the kind of operation without revealing what the operation is about.
Quantitative facts** (2 fields) — `int64Amounts` and `int32Exponents`. **Numerically present but semantically opaque**: Tx-Rails sees the value `12345 × 10⁻²` but does not see whether that means USD, GBP, tonnes of grain, hours of labour, parts count, or kilowatt-hours. The unit and meaning live in your private code.
The Trace contains no descriptions, no party names in plain text, no document attachments, no business semantics. It is the cryptographic envelope around the transaction, designed to be sufficient for integrity verification and nothing more.
The transaction content
The content — descriptions, party identification in plain text, document attachments, free-text fields, payloads, and anything else carrying strategic meaning — is handled separately.
There are four modes in which you can connect your existing applications to our underlay:
The four modes
Mode
What customer does
Metadata fidelity
Use case
A. Hash-only
Nothing; tx-rails computes row-hash from CDC
24 / 33 fields, no semantic intent
Low-stakes flows where the audit just needs “this row existed at time T”
B. CDC observation
Adds Debezium / equivalent to their DB; nothing in the app
27–29 / 33 (CRUD intent from CDC; user/role IF the customer’s table has audit columns)
Mid-stakes flows; most existing apps that already write created_by / modified_by to their rows
C. Gateway-mediated
Routes API traffic through the tx-rails gateway; app unchanged
32 / 33 (gateway sees the HTTP method, the JWT, the body — derives semantic intent from path + verb)
High-value flows; new app deployments where touching the network edge is cheap
D. SDK / atomic dual-write
App calls tx-rails alongside its own DB write
33 / 33 with explicit semantic intent
The very-high-value flows where the customer wants explicit REVERSAL / CORRECTION / RESTATEMENT tagging
In Mode A (hash-only attestation)**, the customer publishes only a hash of the content. Tx-Rails never sees the content. This is the strongest privacy posture and our recommended default for regulated customers.
In Modes B (atomic dual-write) and C (CDC observer), the content can be encrypted on the customer’s side under the customer’s own asymmetric key before it reaches Tx-Rails. We support this configuration explicitly — under it, Tx-Rails handles only ciphertext, and the privacy guarantee matches Mode A.
We make this choice configurable rather than assuming one trust posture suits every customer.
Sharing content with partners
Content destined for a manufacturing partner, regulator, or counterparty uses the same customer-side asymmetric mechanism: encrypt under the partner’s public key, and only the partner can decrypt. Tx-Rails mediates the exchange by validating the corresponding Trace is structurally consistent and the cryptographic commitments line up — without ever decrypting the content itself.
ChubbyChecker — what the cross-checks actually look at
ChubbyChecker’s 132-item audit battery operates on the Trace, not on the content. Each check verifies integrity at the cryptographic and consensus level: signature validity against the validator’s public key, Merkle inclusion of each transaction in the slab root anchored to Hedera, BFT quorum and proposer evidence, operation-code sequence consistency, conservation of amounts across operations that should balance, identity-token consistency across related transactions, slab-anchor cross-references — and many more.
None of these need to see the content. Integrity at the cryptographic and consensus level is fully provable without business-semantic visibility.
Amounts — the one nuance, and the privacy ladder
The amounts fields of the Trace are the only places any numeric information is visible to Tx-Rails. For most customer audiences this is acceptable because the amounts are decoupled from their unit and meaning. For customers whose threat model treats *any* quantitative leakage as a sovereignty breach — defence, aviation, ITAR-controlled goods, competitive-intelligence-sensitive industries — Tx-Rails offers four configurable visibility regimes:
Regime
What Tx-Rails sees
Engineering cost
Typical fit
**Default**
Bare numeric value, semantically opaque
None
Civil aviation, healthcare audit, housing, most commercial verticals
ChubbyChecker continues to verify cryptographic and BFT integrity in all four regimes. The choice belongs to the customer and is made at deployment time.
What stays true across every configuration. Regardless of which integration mode and which amount-visibility regime you choose:
The other 31 fields of the Trace are unchanged.
ChubbyChecker continues to run its load-bearing cryptographic-integrity checks (signature validity, Merkle inclusion, BFT quorum, slab anchoring).
The Hedera slab-anchoring guarantee — cross-jurisdictional tamper-evidence anchored to a public ledger — works identically across all configurations.
The content channel is always encrypted under your key or never seen at all.
The platform’s privacy posture is invariant to your operational choices about which fields to expose for amount-level checks.
“Mobile First” means your distributed application will largely work on iOS & Android tablets, and will work completely for all desktops & laptops. Large-screen-dependent sections of the DApp (eg large interactive Asset Maintenance Budgets) will be difficult to operate on mobile phones and less so on Tablets, yet the rest of your DApp will work naturally for contacts/messages/elastos mail/tasks/calendar integration, for most of your DApp, and for ‘Push’ notifications. Our services are hosted on a global Kademlia network.
For example, we integrate Individual Push Notifications with our mobile apps, to enable your database to instantly remind you of details of contacts (which you select when you are registering them), by a visible ‘Push’ notification to your phone as they ring. This service can be disabled for regular contacts.
You are welcome to refer to our page Computers As Machines for a potted history of Computing, with some references, beginning at the Polish Machine the unsuccessful Bomba, in 1938 (designed attempting to decrypt German Messages encoded with the Enigma Machine), via the successful Bombe (1940) of Alan Turing, who proceeded to design the Theoretical Turing Machine which led (through the British GPO’s Colossus (1943), ENIAC – delivered in 1945, and EDVAC – delivered in 1949) to its practical outcome, “Sequential Machine Architecture” (assisted in consultation by John von Neumann, between 1943 and 1945), followed by the development of the first Operating System for the second generation of those machines (General Motors, 1956, as a customer of IBM).
Also, how the development in 1947 and 1948 under William Shockley, of the first two of the various types of Solid State Transistors, and the accompanying developments in miniaturisation (starting with micro-circuit printing and ion-deposition, ending with the ‘Computer on a Chip’), began the long haul towards the first useful Personal Computers between 1970 & 1980, and away from the need for rooms full of thermionic valves. Then the web, ‘Network-As-Machine’ and Artificial Intelligence, to end with the first Quantum Computer in 2019 (IBM).
“In late 1953, John W. Backus submitted a proposal to his superiors at IBM to develop a more practical alternative to assembly language for programming their IBM 704mainframe computer.[9]: 69 “. Backus, who found the programming of calculations for a Missile Trajectory solver far too tiresome and cumbersome, set about, with a team, designing what was to become one of the first higher level languages (above Assembly Languages), thus making numerically-centred computing much easier, efficient and less error-prone. The language was named FORTRAN, and was first delivered in 1957. The actual meaning of the acronym is disputed, however we have witnessed one historical IBM webpage showing the original acronym to stand for “Formula Translator”.
In 1959 according to the COBOL (Common Business Oriented Language) page on Wikipedia, “a meeting was held at the Pentagon to discuss the creation of a common programming language for business. It was attended by 41 people and was chaired by Phillips.[20] The Department of Defense was concerned about whether it could run the same data processing programs on different computers. FORTRAN, the only mainstream language at the time, lacked the features needed to write such programs”. Some claim the outcome, in the form of the COBOL 60 compiler, can be attributed “95%” to Grace Hopper who designed a forerunner “FLOW-MATIC”. Others (particularly Jean Sammet, who was also involved) disputed this.
The following contains one of the modern lines of ancestry of software in use on computers today (starting around 1970, but still on traditional Sequential Devices, forerunners of today’s sequential personal computers and macs, etc). Note that despite their age, more recent versions of FORTRAN and COBOL are still in use and are relatively popular, the former in numerically intensive engineering and science, the latter in data processing of all kinds, where the natural English forms of commands are valued.
Dennis Ritchie designed and created the ‘C’ Programming Language at the international organisation AT & T’s Bell Labs between 1972-1973
Initially, ‘C’ was for running UNIX utilities. The UNIX operating system was being developed by Ken Thompson at the same time. The pair had previously worked together on the creation of C’s predecessor, the ‘B’ programming language. Evidence of Ken’s and Dennis’s compatibility as programmers and language designers was reinforced, according to Brian Kernighan, when they compared their own independent solutions to a problem which emerged as identical in code. Ken Thompson explained Unix was the first incarnation of a system where everything happened “in the first person”. There was no need for the third eye view – Thompson allowed the Op Sys to do whatever it needed as if the sole controller in the processes. He is a believer in using brute force when necessary (in coding terms).
From the same (criminal-owned) Labs, from 1979 onwards, Bjarne Stroustrup developed C++ as an efficient extension of C , allowing the creation and operation of a new class of things in C (but studied since the 1950’s) called software “Objects”. Objects are Data Structures extended to include the necessary methods (functions) to deal with the data onboard the object, and enabling safer communication between these objects.
In order to get to the present state on personal computers from this point (ie C and C++), you may refer to the Security page on this site which explains the History of the Linux Operating System, as a derivative of MINIX, which itself was designed and sold by Prof Andrew Tanenbaum as a teaching aid (with accompanying software on “floppy disk”) conforming to the UNIX OS Standards, yet able to be compiled onto a PC, making a personal computer into a secure multi-user machine, suitable as a web server (for example). At the same time there were other branches of Open Source activity such as OpenBSD which is based upon the Objective-C language. The differences between the Objective-C approach and the C++ approach to Object Oriented Programming result in certain trade-offs when deciding between languages. Apple’s systems are now based on a fork of the OpenBSD operating system, and do nevertheless allow some programming in C++. Your Mac can also be employed as a multi-user web server, as can a Linux box.
One of the major components in any modern UNIX/Linux Desktop system (and until 20/11/2020, also in Apple Mac OS – now “install on demand” only) is the X11 Window System (a Window System has the same importance for any desktop operating system, though never installed on any production web server). This, together with the user’s choice of “Window Manager”, gives the graphics capabilities and so much of the user-friendliness and convenience of a modern computer. The Window System integrates and gives access to keyboard, mouse/pointing device and screen. Without a system like X11 a personal computer would remain a command line-based thing with very limited graphical capabilities and usefulness.
All of these Software Systems which run on top of other software layers based around the operating system kernel, rely on the compilation of C and C++ source code into machine code for execution on demand (with a strict permission system however!).
During spread of the world wide web in 1995, Netscape Communications recruited Brendan Eich with the goal of embedding the Scheme programming language into its Netscape Navigator.[15] Before he could get started, Netscape Communications collaborated with Sun Microsystems to include in Netscape Navigator Sun’s more static programming language Java (created by James Gosling), in order to compete with Microsoft for user adoption of Web technologies and platforms.[16] Netscape Communications then decided that the scripting language they wanted to create would complement Java and should have a similar syntax, which excluded adopting other languages such as Perl, Python, TCL, or Scheme. To defend the idea of JavaScript against competing proposals, the company needed a prototype. Eich wrote one in 10 days, in May 1995.
More recently (2009), Ken Thompson (see above) et al, working for the Google Corporation, developed the GO language which has simplified objects to exclusively contain what C calls data “struct”s and other data collections such as arrays (so separating functions and data structures once again) and removing inheritance capabilities, in order to achieve very economical thread generation overheads, addressing a modern problem where we are limited, by memory and processor performance, in the number of concurrent threads that we can safely run. These moves also address the problems related to keeping code more easily maintainable and sharable among and between teams. Although inheritance is no longer possible, it is still achievable in practice by cloning, importing or copying and pasting code, by the use of package and dependency managers, and making alterations within a code versioning and sharing system such as github or others. The GO language is used for much of the Elastos “ChainCode”. A GO thread takes a minute fraction of the memory overhead of a JavaScript thread. On
a web server connected to a database, there needs to be one thread per database table, so with hundreds, or possibly thousands, of tables, there are many savings to be made. ITOTCCA had migrated our apps to GO. At that stage, we no longer required 8GB ‘node.js’ servers for our Enterprise Databases. The memory demand had been cut by a factor of 10.
The foregoing discussion is generally taken to apply in a stage of internet development, prior to Blockchains and distributed Applications, now referred to as “Web2”. Although in the newer model, “Web3”, Smart Contract systems such as Ethereum use new languages (eg Solidity) to enable operation of multi-party contracts and transactions, much of the necessary code for the majority of Web3 does not require new languages, with C, C++, GO, JavaScript, Java and Python all in use. The main difference between Web2 and Web3 is that ideally, in a pure Web3 implementation, there is no centralised cloud (or anywhere)-based server. The blockchains act as a continuous transaction journal (not really a Ledger in the Accounting sense), with every device keeping every other device honest by design, including guaranteeing, in associated dApps (distributed Apps), data privacy and ownership. However in the real world, since the demands to store enterprise-sized quantities of data are constrained by computational and network efficiencies, centralised servers are unavoidable. We have merged the Web2 concept (to provide access to Controlled Data Storage) with the Web3 concept, providing decreased risk with increased organisational and operational security, via several layers of decentralisation.
Thus the issue of Trust remains. IBM, India, in a paper published in 2019, have addressed this issue (please refer to: Database Meets Blockchain), and ITOTCCA are following this means of Trust Assurance, using our own Hybrid method, still involving the Elastos Blockchains (“Enterprise Web2/3”).
Front End Graphical User Interfaces
BlockChain &
Component Assembly Runtime
BlockChain &
Tendermint
And our own ChubbyChecker
for Ultimate Data Security
PLpgSQL
Data Processing on Database
Backchecked against the Hiero Blockchain
(Semper FID)
In a similar way to Shipping Containers with Goods, Software “Containers” make life a lot easier for developers: Docker helps in moving software around the world, and with integrating development elements into a single environment.
Further than Docker Containers, the Kubernetes Container-Cluster Management System, based on Docker (if so chosen), assists in integrating development elements, with even more adaptability than by Docker alone, into a single, Cloud-ready development and production environment. In Greek Kubernetes is the Helmsman or “Captain” of a vessel.
Desktop Development Environment from the ceiling – mirrored in Production (from a roof) on at least 2 replicated nodes in the cloud. This single development node (node – shown, on cluster – not shown) is actually inside the desktop computer host (192.168.x.z), on a virtual machine (refer to Install Minikube). Your modem would be at 192.168.x.y on the internal LAN. Minimum requirements for the host hardware are 32GB RAM with 250GB SSD (Solid State Drive)
Across the entire intercommunication system involved above: between the DApps on desktops, laptops, and mobile devices (as well as IoT device DApps) – and the Kubernetes installations in the Cloud, and between the containers and pods within Kubernetes, the Elastos P2P Carrier Network working with Kubernetes Engineering guarantees security.
If you wished to follow this line of development, the multiplexed update function for the master ledger (also known as the general ledger) would need to be encoded in PLpgSQL on the Postgres database(s), which you need to build for your target enterprise(s) (See for example our own Block ‘n ‘ Tackle). This update function (to be run as a trigger function after the entry of data into your financial transaction journal) is not trivial and must allow for full “ripple-up” updating, as transactions are added out-of-chronological-order; and must follow accounting principles, keeping credits and debits balanced structurally and ensuring financial and accounting integrity & consistency on the database in global terms (programatically).Study of a fundamental bookkeeping course is strongly recommended here, as is any possible experience you can attain as a voluntary Treasurer, for example of a local club, society or association. You should be trying to ‘reverse engineer’ any BookKeeping software you use. How does it work? The traditional way for a larger organisation was to store data entered via a system such as IBM’s CICS, networked to a database, with data ‘sliced, diced’ (using SQL – Structured Query Language) and presented in Spreadsheets for reporting and planning purposes. Still a very powerful approach .. yet you do need to decide and iteratively design the structure of the databases you want to employ. Having designed one schema, we multiplied and copied them 9 further times on our test development database system, and ended up with well over 6,000 Tables in the 10 Schema, as an estimate of those required by a more general Full Enterprise-Networked System. The system is currently working for us. You can see the details by clicking here or on the above diagram. Remember: ‘The Database is Everything’. It has taken our 2-man partnership nearly 10 years to get this far, so the task is big, however there are other sections of the project to develop alongside the database, simultaneously.
You would need a table to commence: a financial transaction journal, followed by tables for customers and for suppliers, followed by your general/master ledger (table). How must the master ledger be designed? What fields would you need in the transaction journal? The master ledger? You would also need a Chart of Accounts in some format (some ingenuity required here) which is extensible, and maybe a system of account ‘classes’. How will you handle Invoicing & Charging, Bills, Inventory, Production, Production Scheduling & Quality Assurance, Repairs and Maintenance Scheduling and Response, Payroll, Taxation, Superannuation, Human Resources, Orders Issued and Received, shipments out and in? Do you see why you need to know about Accounting and Bookkeeping, at the very least? There is much more, as this merely scratches the surface, hoping to give leads.You do need to be able to emulate the strategic thinking of a CEO and Board (or manager and owner) about the status and direction of an organisation (and not just financially). There is much to be guided and managed. You are at business nuts and bolts level here. Together with the more top-level interests of Owners and Managers you are simultaneously caring for the software interests of each of the Board (Owner) and CEO’s (General Manager’s) Staff Members. The risks & responsibilities are further extended by entering the multiplexed smart contract market (assuming you do). All participating organisations on a Business Netork require guaranteed individual Protection of Interests at every stage in every process.In order to be able to easily (in a browser-based gui) add tables, columns, primary keys, triggers and trigger functions to the database, consider PgAdmin4. You would be running PgAmin4 within its own container separate to the other containers on your desktop. You have to link it (in the “docker run” statement) to the postgres database server container’s network on docker so it finds the server. All this database development is better done outside/without kubernetes.You would need to become conversant with the docker system and commands. You can update your database structure by regularly performing a pg_dump on the schema. We have found that to “docker push” your images to the cloud regularly is expensive in data, and that it is more reliable and effective to rely on pg_dumps (and restoring the sql dump with psql) in case of system crashes, or just to update, say, a Kubernetes set-up. You “docker pull” to retrieve the bare database image, or let the yml/yaml file do the work. You then enter the container after copying the backup.sql into it from the host, and restore from within the container (see “docker exec”, “kubectl exec”, “docker cp” and “kubectl cp”).Run “minikube start” then “kubectl create secret docker-registry –help”, and issue a command following the pattern at the bottom of the resulting help page. Later, in order to give minikube access to what it considers a secure private repository, you issue ‘minikube cache add your_repo_name/your_image_name:your_tag’ then ‘minikube cache reload’. You can list your cached repo’s with ‘minikube cache list’. We have found that attempting to rely on the database image as-cached may be disappointing, and that the restore-from-backup procedure above is necessary on the cached images, after they have been built and are running as containers.“minikube start” needs to run with no error or warning messages at all. If there is a message you need to attend to the errors noted (particularly enabling non-root usage of docker as recommended in the message, exactly). Then “minikube stop” and “minikube delete”. Then “minikube start”, followed (because of the delete) by your “kubectl create secret docker-registry, etc” command, again (and after each delete/start).The kubernetes system builds a node from pod and pod-service specifications in a .yaml file (kind of similar to a docker-compose.yml file) which is run with “kubectl apply -f path/to/filename.yaml”. The pod and service specs contain the specs for each container in that pod, and the entire yaml file may contain several pod specs, each with their own containers specified.In our Kubernetes System modeled above, there are 3 ‘deployment’ specs involved, consisting of 2 replicated pods each. One pod houses the elastos blockchain set of 4 containers; and the others the in-memory cache and webserver. The database consists of 2 replicated pods in a Stateful Set. Overall, each deployment or stateful set spec has its own service specification prepended, thus there are 4 services defined, containing the specs of port numbers exposed on each container in each pod. For the stateful set, its node service is a ‘Headless Service’, which is never exposed externally.If you intended to develop a secure system, consider utilising the Elastos Development Tools and Environment. These tools and environment provide a way to develop Ionic front end gui’s as well as giving access to the necessary blockchains. The key to accessing your database from the Elastos DApps securely is to specify the url as service-name:port/* of your database’s webserver in src/assets/manifest.json (in your Elastos/Ionic DApp’s root folder) together with all necessary url’s to be accessed. In our case, for the database webserver the url is haskell:webserve-https/*. (The port name, here ‘webserve-https’, should be used, not the Port Number, similarly with service-name in place of the IP-Address.)
We develop code and collaborate on Atlassian’s Bitbucket – more of the work of Australians!
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A short word about our apps….
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As server and general system response times are partially dependent on the relative locations of the Data Centre (the Cloud Centre) and the client, at this stage we are planning to use Sydney and London as our Data Centres.
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Immutable audit trails and multi-party transactions on Hedera Blockchain; mass Relational Data storage on Postgres
Databases: kademlia network to connect you to them (web-socket-safe).
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by John L. Olsen, Edward B. Whittle
Connecting to Databases, WebServices and the IPFS file storage system via Kademlia
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Our MultiPlexed Double-Entry Accounting System
Master Ledger::Transaction Journal catering for Multi-Party Transactions on the BFT-SMaRt/PostGIS System
ITOTCCA’s Accounting Solution,
the ‘Block ‘n’ Tackle’™
.. incorporating a convenient Business Process Design Interface ..
Written in JavaScript, C++, GO, Python, Java, Solidity, and PLpgSQL on Hiero BlockChains, Java Servers and Postgres Databases
Tx-Rails is based on a combination of the original Byzantine Fault Tolerant Tendermint Project (no mining) supervised by ITOTCCA’s own ChubbyChecker, but where ChubbyChecker is operated by an Arm’s-Length arrangement with a second company, involving protocols preventing the sharing of operational cryptographic keys between parties, ie separating control of enterprise-side metadata (ITOTCCA) from auditing-side metadata (the operators of ChubbyChecker). That arrangement itself is independently audited yearly.
Anchoring DLT
The frontend Apps can be encoded in HTML (Content & Structure), JavaScript (Function) and CSS (Style), and cross-compiled for Native (iOS, Android) Mobile Machine Codings, from Use Cases defined by You and us. We use the Ionic Framework in connection with the Elastos.Essentials System (providing registration/authentication services in connection with Distributed ID chains) to target all your platforms; smartphone, tablet, laptop and desktop.The use of JavaScript and its style of coding – a relatively recent new ‘paradigm’ in enterprise applications programming (with the improvements in performance introduced in the 2008 release of Google’s V8 JavaScript compiler), ensures maximum speed and availability of running applications due to their design of “Non-Blocking Input/Output”.Input and output channels for the devices & servers, and for the databases, are fully occupied, never blocked or waiting for one or other user’s slower process to complete. Slower processes are immediately resumed upon call back. (“Callback” functions are an important feature of JavaScript). This means bandwidth is used very efficiently and processes are lightning fast.
Our method of development ensures 100% functionality on all iOS and Android mobile tablet devices, and communication, contacts, tasks, calendar etc (plus ‘Push’ notifications) integration for mobile phones.
This functionality is easy to generalise to desktop/laptop computers.
It all just works. And fast.
Also note that “JavaScript” is not “Java”; they are owned and licensed by 2 very different realms amongst software corporations. Strictly speaking, Java™ came first historically, owned and created by James Gosling of the now defunct Sun Microsystems in 1995 (bought by Oracle in 2010). JavaScript was created by Brendan Eich in 1995, in competition with other companies, to complement Java, for an old company, Netscape Communications.
Mozilla Foundation took over management of JavaScript in 2000. However with the release of Google’s (Enterprise-capable) V8 JavaScript Compiler in 2008 (see “Nodejs”), the two languages are now “complementary/competitors” in the web-based-design software language market. The languages can and do work together in many systems, including our own.JavaScript’s particular strength is in its “asynchronous” operation. Java usually operates “synchronously” and is thus “I/O Blocking”. Although its kernel is a Linux Kernel (in C/C++), much of the Android™ Operating System, for example, is written in Java™ or utilises access to Java Libraries. This means our own systems contain machine code (it’s all machine code in the finish – imagined as 1’s and 0’s in memory registers) compiled from these “higher level languages” Java and JavaScript, as well as our webservers in the Python language. There is also machine code from the compilation of Assembler, C, C++ (non-Apple devices) and Objective C (Apple devices) lower level software code.On a typical webpage, JavaScript takes care of the “behaviour” of the page (said to be the ‘glue’ of the web, otherwise thought of as the ‘functional’ part), while structure and content is given in the “html”, with style/ formatting defined in Cascading Style Sheets (CSS).It all starts in various garbled forms of English written in a very conformal fashion by many people; compiled and built on some computers; and ends by being “deployed” onto these and other computers, and onto networks of other devices, to begin working, making decisions, accepting inputs, transferring data, producing outputs, recording information and using Digital Arithmetic and Boolean Algebra, the hardwired “machinic intelligence” of a computer (and recently, the plastic Artificial Neural Networks of AI), to perform logical, numerical and linguistic computations. ]
Tx-Rails — Built by IT/OT Chain & Cloud Australia (ITOTCCA). Sydney · London.
Also, how the development in 1947 and 1948 under William Shockley, of the first two of the various types of Solid State Transistors, and the accompanying developments in miniaturisation (starting with micro-circuit printing and ion-deposition, ending with the ‘Computer on a Chip’), began the long haul towards the first useful Personal Computers between 1970 & 1980, and away from the need for rooms full of thermionic valves. Then the web, ‘Network-As-Machine’ and Artificial Intelligence, to end with the first Quantum Computer in 2019 (IBM).
And our own ChubbyChecker
for Ultimate Data Security
Databases: kademlia network to connect you to them (web-socket-safe).
Reverse-Proxy
