No-Code Super App Platforms: Why Turing Completeness Matters

Building a super app builder is no easy task, but attempting to build a no-code super app builder is an entirely different level of complexity. The amount of entropy and uncertainty that goes into such a project is off the charts. And yet here we are, not just talking about building it, but about building it right.

Software development has come full circle. Today’s business leaders are more tech-savvy than ever before, while many engineers are focused on tasks that have little to do with real engineering. Ask yourself: How many computer science graduates, two years after earning their degree, can name five sorting algorithms—and code at least two from scratch?

Everything seems to be merging into one giant commercial bubble—startups raising money without a clear problem to solve, founders pitching misaligned visions for years, and companies somehow securing funding without delivering real value, only to collapse a few years later.

But today’s article isn’t about startup hype cycles or product failures. It’s about something deeper—something often overlooked: a critical factor that determines whether a platform tackling a near-impossible challenge can succeed.

Let’s be clear: building a super app is one of the hardest challenges in software. But building the tools to build that super app—a no-code super app builder—is arguably just as hard, if not harder. How do you do it? Work hard, add components, and hope for the best?

Not even close.

In every layer of complexity, there’s a scientific core—a Eureka moment of clarity. A no-code super app builder must be deeply grounded in computer science principles to anticipate and handle the unknowns that future users will throw at it.

The platform has to be a beast: robust, scalable, adaptable, and mature—delivering reliable software, bug-free, and on time.

And above all else?
1.0  It must be Turing complete if it hopes to succeed.

Let’s unpack that bold claim.

First, a quick refresher: What does ‘Turing complete’ mean?

The foundation of Turing completeness originates from Alan Turing’s seminal 1936 paper, “On Computable Numbers, with an Application to the Entscheidungsproblem.” In this groundbreaking work, Turing introduced the concept of a universal computing machine—now known as the Turing machine—which formalized the idea of algorithmic computation and set the theoretical limits for what machines can compute.

Alan Turing Work – Cover Page

While Turing himself did not use the term “Turing complete,” his work remains one of the most cited in computer science history. It’s unclear who first provided the formal definition, and today, there are some differences in how Turing completeness is described across various sources. For clarity, we will reference a definition from Michael Sipser’s book Introduction to the Theory of Computation, which states:

“A computational model is Turing complete if it can simulate a Turing machine. That is, it can compute any function that is Turing-computable.”
(Sipser, M. (2012). Introduction to the Theory of Computation, 3rd Edition, Cengage Learning.)

2.0 Turing Completeness & Visual Programming:

Originally no-code was associated with visual programming and some research had been done to investigate the relationship between Turing Completenes and Visual Programming:

(If you’re new to the concept of no-code platforms, check out What is a No-Code Platform? Five Key Parts Explained for a solid foundation.)

• Scratch –  Scratch is a block-based visual programming language developed by MIT’s Lifelong Kindergarten Group. It’s designed primarily for education, enabling kids and beginners to create interactive stories, games, and animations without typing code.
• Blockly – Blockly, created by Google, is a web-based visual programming library that lets developers build block-based coding editors (it’s like the “engine” behind other tools). Blockly can be customized to generate code in multiple text-based languages like JavaScript, Python, PHP, and more.
• Unreal Engine’s Blueprint – the visual scripting system built into Unreal Engine, one of the world’s most powerful game engines. It allows developers to build complex game logic using a node-based visual interface, without writing C++ code.

All three of these tools—Scratch, Blockly, and Blueprints—are visual programming systems that abstract away textual coding. Research into their Turing completeness shows that visual tools can indeed be fully computationally expressive, provided they include:

• Loops/recursion
• Conditional logic
• Variable/state management.

So that is a good start, as most No-Code platforms have human interfaces based on some type of visual language expressions. 

3.0 No-Code Limits.

Now things get a little more complex, as today’s No-Code system often hits some limitations when it cannot handle advanced topics. That immediately deviates from the implementation, and low code becomes your friend. We are not talking about those here.

4.0 Super App Builder Framework

We will focus only on the No-Code platforms as a foundational vehicle for delivering super apps.
And of course, with the help of AI and auto-generated components, whether in design or run-time, to enable plug-and-play functionality. (For a detailed look at what defines a super app, see What is a Super App?.)

One question we need to answer before we continue: What’s the “Threshold” to become Turing Complete? You often hear, Hey, my protocol, or my language, or my system is Turing Complete. In our case, we would look for just three points a system must have:

4.1 Conditional Logic (if/else),

4.2 Ability to loop/repeat (iteration or recursion),

4.3 Ability to manipulate and store variables (memory/state).

At ACENji, we focused on those three components very deeply. State is a highly complex topic, and we may need to publish a separate article focused entirely on state management, covering everything from individual element-level state to page-level and multi-page state. 

Here are some examples to show the difference when it is not and when it is Turing Complete

4.4  Only Conditional Logic (but No Loops) → Not Turing Complete:

          IF user.age > 18 THEN show “Adult”

          ELSE show “Minor”

As we see, this is a decision tree, not a full computation

4.5 Only Loops (but No Variables) → Not Turing Complete:

          REPEAT 5 TIMES:

          show “Hello!”

You cannot modify behavior or state

4.6 Loops + Conditional Logic (but No Variables) → Still Not Quite Turing Complete:

          FOR each item in shopping_cart:

          IF item.price > 100 THEN show “Expensive”

It is bounded by the input

4.7 Conditional Logic + Loops + Variables (Turing Complete):

Fibonacci Sequence Generator

          LET a = 0

          LET b = 1

          WHILE true:

          output a

          temp = a + b

          a = b

          b = temp

It stores memory (a, b) and uses logic + looping.

And here is something funny, Excel formulas on their own are NOT Turing Complete, but with the recombination of macros, they are!

We are not quite done with that part. 

5.0 Same topic from mathematical rigor

5.1 Finite State Machines (FSMs)

They are not Turing complete. A math model for FSMs is defined as 5-tuple: M = (Q, Σ, δ, q0, F), where:

• Q: finite set of states
• Σ: input alphabet
• δ: transition function (Q × Σ → Q)
• q0: start state
• F: accept states

Limitation:

• No memory (finite state only).
• Can recognize regular languages (REGEX), but cannot count arbitrarily.

Example: FSMs can check if a string contains ‘ab’, but can’t check if a string has the same number of a’s and b’s.

5.2 Pushdown Automata (PDAs):

Adds a stack (1 form of memory) but is still NOT Turing complete. The math model is defined as a 6-tuple:M = (Q, Σ, Γ, δ, q0, F),

Where:

• Γ: stack alphabet
• δ: transition function (Q × Σ × Γ → Q × Γ*)

Recognizes context-free languages.
Example: can match balanced parentheses (e.g., ((()))).

Limitation:

• Can’t handle multiple stacks or arbitrary memory.
• Can’t solve things like palindromes with arbitrary length in two sections (you need more memory).

5.3 Turing Machine (TM):Fully Turing complete. 

The math model is defined as a 7-tuple

M = (Q, Σ, Γ, δ, q0, q_accept, q_reject)

Where:

• Γ: tape alphabet (incl. blank symbol)
• δ: (Q × Γ) → (Q × Γ × {L,R}) → changes state, writes symbol, moves tape.

Key:

• Has infinite tape (unbounded memory).
• Can run indefinitely.

The threshold to go from “not Turing complete” to “Turing complete” is:

6.0 Church-Turing Thesis

The Church-Turing thesis asserts that:

“Anything that can be computed algorithmically can be computed by a Turing machine.”

Which directly implies that a no-code builder—whether for super apps or not—cannot truly claim to build any app unless it can express every algorithm, which requires Turing completeness.

In practice, however, there’s an important caveat: what happens if we encounter an infinite loop? Can your no-code system still handle that and remain Turing complete? Theoretically, yes. But in real-world applications, we need to put additional safeguards in place to prevent infinite loops—otherwise, the system risks wasting resources and energy on computations that never terminate.

With that in mind, let’s restate the key definition:

“The mathematical tipping point is simple: to cross from a finite state machine to Turing completeness, a no-code builder must introduce both unbounded memory and the ability to loop over it—a leap that moves it from processing regular or context-free languages into the full power of algorithmic computation.”

7.0 Minimum Feature Set for Turing Completeness:

Must-Have Features:

Infrastructure Add-ons:

Optional (but Highly Recommended):

It takes some time for a platform to become Turing-complete.

8.0 Case study comparison:

Bubble.io: Took ~2–3 years to mature to Turing completeness.

Adalo/Webflow: Initially non-Turing complete—gradually added more logic over time due to customer demand.

ACENji also took about two years to mature to Turing Complete.

But in my opinion, having the three points may not be enough to be a practical Turing-complete system for building super apps. Something else is missing from the menu, and that is what is known as “Defence in Depth” or DiD.

If we take, for example, how a nuclear plant is built with DiD in mind, Defense in Depth means the plant is designed with multiple independent and redundant layers of safety systems so that if one fails, others will catch the failure.

For example:

• Physical barriers (fuel cladding, reactor pressure vessel, containment building)
• Engineered safety systems (cooling systems, emergency shutdown systems)
• Administrative controls (procedures, training)

How DiD applies in our NoCode example tool strategy: it may be design to stop infinite loops or prevent logic crashes. For example, a logic engine in no-code might need DiD to prevent runaway logic.

There could be a need for one more layer of defence: Byzantine fault. A multi-tenant no-code platform (with decentralized plugins/services) might need Byzantine fault tolerance to maintain data integrity.

There is also an interesting aspect of which layer would prevail during the design of the Super app builder: the visual representation of all elements, their combinations into tiles, pages, and workflows vs actions, algorithms, looks, and state management. That dilemma may be solved in the future by the consumers as they are eventually going to figure out what their threshold is for tolerating pivoting away from Turing complete, even if they have no idea who Alan Turing is, a consumer knows perfectly well when an app fails to deliver.

In the end, the question isn’t just whether your no-code super app builder can look good or ship fast—it’s whether it can truly stand the test of complexity. Turing completeness isn’t a theoretical nicety; it’s a non-negotiable foundation if you’re serious about enabling users to build apps that can evolve, adapt, and scale without hitting a wall.

Visual interfaces, intuitive workflows, and clever design matter—but without the computational muscle of Turing completeness, every no-code platform will eventually show its limits. That’s why the next generation of super app builders must blend usability with deep technical rigor.

The challenge is steep—but the payoff is transformative. The platforms that succeed will empower creators not just to build apps, but to build anything they can imagine.

And that’s the true promise of no-code: unlocking the full power of computing—without writing a single line of code.

What to explore more of the hidden words of super apps and no-code – check What Modern No-Code Platforms Inherited from Lotus Notes.

Thanks for reading,

Ivan Assenov