DEV Community: Glab Strizhkov

Interconnected Cyclical Loops as a Method for Constructing Artificial Subjectivity

Glab Strizhkov — Mon, 19 Oct 2026 19:02:10 +0000

With this post, I would like to begin a series dedicated to the attempt to apply certain neurobiological patterns to the design of computational systems. In it, I will explore a simple concept that would allow us to build systems whose behavior, from an observer’s perspective, would be subjective.
First of all, let’s agree on some terms so that we can all be on the same page:
A queue is a data structure based on the first-in, first-out (FIFO) principle. An element added to the queue ends up at the end of the queue. An element removed from the queue is located at the front of the queue.
A circular loop is a data structure based on a queue in which the last element added becomes the next one relative to the first, forming a loop. In such a structure, the direction of traversal remains crucial; specifically, the FIFO principle is followed, provided that the elements remain in their places, and the FIFO principle determines the order of traversal of such a structure.

Two or more queues between whose elements there are relationships outside the defined data structure, but which do not violate it, are called linked.

A class is a template according to which an object is created.

A class object is a concrete implementation of a class with its own set of data and state.

Queues form the basis of this concept because they are extremely convenient for packaging polymorphic tasks for CPU computation, since any complex data structure can be represented as a sequence of its traversals. For example, an audio file can be represented as a sequence of frequencies, and a car’s design as two sequences: a sequence of components and a sequence of connections. The car’s design, represented by these two sequences, is an example of linked sequences, since they are connected by a single common rule—there are connections between the components.

Returning to the cyclic circuit, if we view it as a data structure that we traverse in a loop, we find that it provides a convenient way to describe cyclic and rhythmic processes. Weather patterns, the alternation of day and night, and certain processes in the central nervous system—such as the functioning of short-term memory, anatomically represented by the Peipets circle.

If we assume that each element of a cyclic circuit—rather than being as simple as a number—is represented by a more complex structure, such as a class object, then we can construct a pair of interconnected cyclic circuits in which traversing one circuit will affect the state of the other and, consequently, the result of its traversal. This principle formed the basis of the design of the German Enigma cipher machine, which was a highly sophisticated algorithm for generating pseudo-random data at the time. In this case, “data” refers to a sequence of rotor positions at a specific moment that is difficult to predict. The rotors of the cipher machine formed precisely those interconnected cyclic loops discussed in this article.

Given that each element of the system can be quite complex, its behavior can affect the system by accelerating, slowing down traversal the circuit to which it belongs, or, in general, by allowing or prohibiting further bypassing if the system implements such behavior options. The connections between elements of different loops can alter the behavior of the elements themselves. Thus, two or more interconnected loops can form a system similar to a gyroscope in a moving environment, where the first loop models the rotor and the second models the suspension system in the surrounding environment. The surrounding environment can affect the second loop, and without the first loop, it would be easily influenced; however, a heavy first loop spun up to a high angular velocity will influence the second loop in such a way as to prevent changes in its behavior in response to environmental influences. Some navigation equipment operates based on this principle, and in orbit, more massive gyroscopes influence the trajectory of artificial satellites.

In the context of software, this principle can be used to construct a stable but not constant behavioral model. The ratio of the relative influence of the first and second loops in a pair can make the system more or less flexible. Combining a larger number of cyclic loops can lead to more complex, either predefined or random, behavioral models.

These properties can be used to simulate creativity and the human factor, and—in a sufficiently advanced implementation—can be interpreted by an observer as subjectively driven actions.

Such non-objective decisions can be useful primarily in two areas:

Testing. Generating human-like product usage scenarios. This allows us to go beyond the logic intended by the developers and identify vulnerabilities and imperfections at an early stage.

When working with large language models, in cases where it is necessary to make them more natural. To throw them off their usual path, add an element of randomness to the pipeline, or recalibrate how the model is influenced by the outside world—that is, the user.

This article provides a general overview. In the following parts of the series, we will examine in greater detail the components from which such systems can be built and the specific use cases that may be useful when choosing—as a baseline—the modeling of mental processes and social behavior.

A Gigabyte for Fifty Megabytes

Glab Strizhkov — Fri, 11 Sep 2026 11:04:09 +0000

Why thousands of small projects create a large false demand for calculations and what to do about it

Data centers are more expensive than ever before—and it’s not just about the money. According to estimates by the International Energy Agency, data centers consumed about 415 terawatt-hours of electricity in 2024—roughly 1.5 percent of global consumption—and this figure has been growing by 12 percent annually. Under the IEA’s baseline scenario, this figure will double by 2030 to about 945 terawatt-hours, and in the U.S., the expansion of data centers will account for half of the total increase in electricity demand. Simply put, what used to be a minor item on the planet’s utility bill is becoming one of the main burdens on the power grid.
This raises an important question: How much of this demand is genuine? How many of the rented servers are actually performing a useful task, and how many are simply taking up space in the rack and in the invoice?
I want to talk about a telling example of this mismatch. Thousands of educational, hobby, and DIY projects that, in practice, use tens of megabytes of RAM, even though a gigabyte is reserved for each project. On the scale of a single project, this is a trifle. But there are hundreds of thousands of such projects, and collectively they amount to what I will henceforth refer to as “false demand for infrastructure”: demand that does not correspond to any real load.
Let's start by saying that the discrepancy between "rented" and "used" is not my guess, but a measured fact of the industry.
Every year, the Flexera State of the Cloud survey polls hundreds of technical leaders and consistently finds that a significant portion of cloud spending is not justified. In 2026, this share was 29%; in 2019–2020, it was 30%; in 2021, 32%; in 2022–2023, 28%; and 27% from 2024 to 2025. The stability of this metric is the key takeaway here: this is not a glitch; it is a structural feature. According to Gartner’s calculations, 675 billion dollars were spent on nothing. According to Harness’s estimate (FinOps in Focus 2025), the figure is 44.5 billion in a single year.
Flexera directly identifies the reason why this trend has persisted for years: fewer than half of developers have access to real-time data on idle resources, and only one-third can see whether a workload is overprovisioned. You can’t optimize what you can’t see.
The problem becomes even clearer when you look at the hardware level. A McKinsey study showed that servers in corporate data centers are rarely utilized at more than 6% of their capacity. And research by Jonathan Coomey (Stanford) in collaboration with the Anthesis Group found that 30% of them are “comatose.” These servers were consuming electricity but had not performed any useful work for half a year or longer. On a global scale, that amounts to 10 million servers, or 30 billion dollars in idle costs. The Uptime Institute independently arrived at similar figures.
The industry has long been systematically paying for what it doesn’t use. A hobby project with a gigabyte-sized instance for a 50-megabyte database isn’t an anomaly—it’s a miniature version of the problem.
Here the objection arises: “So what? The provider sells memory with a margin and will give my surplus to someone else. No one loses anything.” It sounds convincing, but such a statement is false.
In 2007, Luis Barroso and Urs Höltze of Google, in their article “The Case for Energy-Proportional Computing,” articulated a problem that still plagues us today: an ideal machine would consume no energy when under no load. But real hardware isn’t capable of that. Measurements show just how far it falls short. According to data from the U.S. Department of Energy, the typical server load is 10–15%. Nevertheless, a server’s power consumption when idle reaches 60–80% of its power consumption under load. More up-to-date figures from the Kepler project at the CNCF, as shown by the SPECpower benchmark, range from 20% to 60% of the maximum, depending on the hardware generation. It is important to keep this key fact in mind: a 10-fold reduction in load does not result in a 10-fold reduction in power consumption; it remains closer to the midpoint of the maximum range.
What can be done about all this? The most promising answer is to share computing resources. This involves bundling a large number of small projects into dense clusters on a shared infrastructure, rather than providing each one with its own dedicated instance with excess capacity.
With this approach, it’s important to be honest with yourself. The idea isn’t new. Shared memory and computing are implemented in Neon and PlanetScale, while thousands of small SQLite instances run on shared infrastructure—Cloudflare D1. Dense workload orchestration is the bread and butter of orchestrators like Kubernetes.
So the problem isn’t technical; it lies in defaults and incentives. Why does “1 project – 1 instance” remain the standard for a startup, a project under development, or even a side project? Because that’s what most manuals and tutorials aimed at a broad audience say. Because “scale-to-zero” seems like “advanced” magic. Because no one is personally accountable for an empty gigabyte—it gets lost in the hosting bill.
Dense packaging comes at a price that can’t be ignored: weak isolation compared to classic virtualization, the problem of noisy neighbors, greater difficulty in building reliable security, and significant losses in the event of a “shared” machine failure. The point of this article isn’t that “Oh, I came up with a solution”; it’s that the industry—and each of us—regularly finds itself at a crossroads: pay for the convenience of the default, or use resources efficiently. Unfortunately, we still often automatically choose the former.
Let’s go back to where we started. The day is approaching when data centers will consume 3% of the world’s electricity, and in certain regions, they will account for half of the total increase in electricity demand. Against this backdrop, the conversation is no longer about saving $5 on hosting services. Now it’s a conversation about engineering ethics: we’ve gotten used to ordering with a margin of safety. That margin comes at the cost of electricity and hardware production.
False demand isn’t reduced by heroic decisions, but by working on default behaviors—calculating actual needs rather than ordering blindly. Opt for “scale-to-zero” where the load is sporadic—and in pet- and other projects of a similar scale, it’s always sporadic.
Cooperation on this issue could be the solution. One gigabyte of freed-up RAM or one CPU core won’t make much of a difference, but hundreds of thousands will.

Sources:
https://doi.org/10.1109/MC.2007.443
https://www.eweek.com/servers/30-percent-of-servers-worldwide-sit-idle-report-says/
https://councils.forbes.com/blog/strategies-to-manage-cloud-spend-post-cloud-boom
https://spendark.com/blog/state-of-cloud-waste-2026
https://tag-env-sustainability.cncf.io/blog/2024-06-idle-power-matters
https://www.flexera.com/about-us/press-center/flexera-finds-cloud-value-is-rising-while-ai-waste-grows