I used Codex to customize my Hyprland desktop – and learned a valuable AI lesson


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Jack Wallen/ZDNET

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ZDNET’s key takeaways

  • Configuring the Hyprland window manager is challenging.
  • I wanted to see how well AI would perform at creating a .conf file.
  • There’s an important lesson to be learned here.

This is the year I realized how much I enjoy tiling window managers, and Hyprland is my favorite so far.

If you’ve never experienced a tiling window manager, know that they aren’t exactly for the faint of heart. They are driven by keyboard shortcuts, of which there are a lot to memorize. On top of that, most of them require configuration via text files.

Such is the case with Hyprland.

Hyprland is configured via the ~/.config/hyrp/hyrpland.conf file; for the uninitiated, it can be rather daunting. You really should know what you’re getting into before you make that first edit to the file.

Also: How to install Arch Linux without losing your mind

I’ve done a bit of Hyprland dotfile ricing (a fancy way of saying I’ve spent time customizing hyprland.conf files), but I decided to run a little experiment.

I wanted to see if AI could create a hyperland.conf file based on my prompt. I decided to start fresh with a CachyOS installation. (I selected both the Hyrpland and KDE Plasma desktops — why I added both will become clear shortly.) Once I had CachyOS up and running, I started the process. I decided to try three different AI tools: Opera’s Aria, Ollama, and Codex.

Of the three AI tools, Codex was the only one capable of creating a remotely usable configuration. Here’s how it went.

The prompt

To create the customization, I used the following prompt:

Create a hyprland.conf configuration file for Hyprland version 0.55.2 that uses Waybar with a glassy, rounded-corner theme, a color palette of purple and pink, and uses the following keybindings: Super+t to open the terminal, Super+b to open the web browser, and the default keybindings for moving windows and window focus.

Upon running the query, every AI service I used informed me that many configuration options would be placeholders and that I would need to customize them to fit my needs.

Also: 7 AI coding techniques I use to ship real, reliable products – fast

It took a few tries, but eventually Codex gave me this .conf file, which you can view in my GitHub repository.

I had my doubts that it would work. Even so, I added the contents from Codex and reloaded the window manager with:

hyprctl reload

I was not surprised to encounter numerous errors. I’d spotted some of the errors even before I copied the output to the .conf file, but wanted to see what happened regardless.

Hyprland

This is just a sample of the errors in the Codex-generated config file.

Screenshot by Jack Wallen/ZDNET

Here are the problems I found at first glance:

  • There was no default terminal set.
  • The border_radius option no longer works in 0.55.2.
  • In the rounding = 12px option, the px would cause an error, so it had to be removed.
  • Windowrule does not work.

Also: The best Linux laptops you can buy

I also had to install the following to make this work:

  • kitty terminal app
  • Waybar
  • rofi

Without the above installed, Hyprland wouldn’t be very functional.

After resolving the above issues, I was surprised that the .conf file worked. It wasn’t a very elegant desktop, nor did it apply the color scheme I added in my query, but I had a skeleton .conf file I could use to further tweak.

Why did I install KDE Plasma?

When I first set out to do this, I only installed Hyprland on CachyOS. After adding the contents to the config file as-is (because I wanted to see how it worked), I wound up with what was essentially a non-functioning desktop. The main reason for this is that kitty, Waybar, and Rofi were not installed.

Also: KDE Linux is the purest form of Plasma I’ve used in months – but there’s a catch

I installed CachyOS a second time, only with KDE Plasma along for the ride. With KDE Plasma added, I knew I had a backup desktop environment to use, should things go awry. All I had to do was reboot CachyOS, log into KDE Plasma, and fix the issues.

Once I had all of the issues fixed within the Codex-derived .conf file, Hyprland worked as expected. Of course, there were a lot of tweaks that needed to be taken care of to get it exactly how I wanted it to look, 

A lesson learned

I was fairly certain how this experiment would turn out, and it solidified my opinion that AI is often wrong but can at least serve as a launching point. Even though I explained to Codex (the only AI to come close to creating a functioning hyprland.conf file) which version of Hyprland I was using, it still used options that are no longer viable.

Also: GNOME 50 is a brilliant release – but I had to look twice to see why

To those who might be interested in migrating to the Linux operating system, I first want to say that Hyprland is not the window manager for you. Stick with KDE Plasma, Cinnamon, or GNOME. For those who want to try and use Hyprland (or any tiling window manager configured with a text file), consider AI to be a means to see how the configuration files work, but make sure to learn from what AI presents to you, so you can understand how it works and can start creating files on your own.

Think of AI as nothing more than a means to take your first steps, but know that you will have to correct its mistakes.





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Embodied Intelligence and the Phenomenology of AI explores how human cognition arises from perception, embodiment, and experience in contrast to disembodied artificial intelligence.

Conceptual diagram illustrating embodied intelligence and the phenomenology of AI through perception, embodiment, environment, and experience.

A Conscious Intelligence Perspective

The rapid development of artificial intelligence has transformed modern discussions about cognition and intelligence. Machine learning systems now recognize patterns in data, generate language, analyze images, and assist with complex decision-making processes across scientific, economic, and technological domains. These capabilities have led some observers to suggest that artificial systems may eventually replicate or even surpass human intelligence.

Yet beneath these technological achievements lies a fundamental philosophical question: what does it mean to be intelligent? While artificial intelligence can perform impressive computational tasks, human cognition emerges from a far more complex interaction between perception, embodiment, and lived experience. Understanding this distinction requires examining the concept of embodied intelligence—the idea that human cognition arises through the dynamic interaction between mind, body, and environment.

Phenomenology, the philosophical study of conscious experience, offers a powerful framework for understanding embodied intelligence. Rather than treating cognition as a purely abstract computational process, phenomenology emphasizes that perception, thought, and understanding occur within a lived world shaped by sensory experience and bodily engagement. When applied to contemporary discussions of artificial intelligence, this perspective reveals important differences between human cognition and machine intelligence.

Within the framework of Conscious Intelligence (CI), embodied intelligence highlights the experiential foundations of human awareness and interpretation. It underscores why human cognition remains essential in guiding technological systems, particularly as artificial intelligence continues to expand its capabilities.

Understanding Embodied Intelligence

The concept of embodied intelligence challenges traditional views of cognition that treat the mind as an abstract information-processing system. Early models of artificial intelligence often assumed that intelligence could be replicated through symbolic reasoning and computational logic. According to this perspective, cognition could be understood as the manipulation of symbols according to formal rules.

However, research in cognitive science and philosophy has increasingly shown that human intelligence cannot be separated from bodily experience. Perception, movement, and environmental interaction play fundamental roles in shaping how individuals understand the world (Varela, Thompson, & Rosch, 1991).

Embodied intelligence suggests that cognition arises through continuous engagement between the organism and its environment. Rather than operating as a detached reasoning system, the mind develops within the context of sensory perception and physical action.

Consider a simple example: observing a bird in flight. This experience involves more than visual pattern recognition. The observer’s body subtly adjusts posture, attention tracks motion through space, and prior experiences shape expectations about movement and behavior. The act of perception becomes an integrated process involving vision, spatial awareness, memory, and anticipation.

This dynamic interaction between perception and action forms the basis of embodied cognition. Intelligence emerges not from isolated computation but from the ongoing relationship between body and world.

Phenomenology and the Lived Body

Phenomenology provides a philosophical foundation for understanding embodied intelligence. While early phenomenologists such as Edmund Husserl explored the intentional structure of consciousness, later thinkers emphasized the central role of the body in shaping perception and cognition.

The French philosopher Maurice Merleau-Ponty argued that human consciousness is fundamentally embodied. In his influential work Phenomenology of Perception, he described the body as the primary site through which individuals encounter the world (Merleau-Ponty, 2012). Rather than functioning as an object separate from consciousness, the body becomes the medium through which experience unfolds.

According to Merleau-Ponty, perception is not merely the passive reception of sensory data. Instead, it is an active process in which the body engages with the environment through movement, orientation, and attention. The body provides a framework through which space, time, and meaning become intelligible.

This perspective challenges purely computational models of intelligence. Artificial systems may process visual data or recognize objects in images, but they do not experience the world through a lived body. They do not move within environments, feel spatial relationships, or engage with objects through physical interaction.

Phenomenology therefore highlights a crucial distinction between human cognition and artificial intelligence: human intelligence is grounded in embodied experience, while most AI systems operate within abstract computational environments.

The Limits of Disembodied Artificial Intelligence

Modern artificial intelligence systems excel at tasks involving pattern recognition and data analysis. Deep learning networks can identify faces in images, translate languages, and predict complex trends based on large datasets. These capabilities have created the impression that machine intelligence may soon approximate human cognition.

However, AI systems typically operate in disembodied informational spaces. They process data within computational architectures rather than through physical interaction with the world. Their “perception” consists of numerical representations rather than lived sensory experience.

Philosopher Hubert Dreyfus argued that early AI research underestimated the importance of embodied and contextual knowledge in human cognition (Dreyfus, 1992). Humans navigate the world through intuitive understanding shaped by years of bodily interaction with their environment. Much of this knowledge remains implicit rather than formally articulated.

For example, people can effortlessly grasp objects, maintain balance while walking, or recognize subtle emotional expressions in social interactions. These abilities arise from complex sensorimotor systems that integrate perception and action.

Replicating such capabilities in artificial systems has proven extraordinarily challenging. While robotics research has made significant progress, the embodied adaptability of biological organisms remains difficult to reproduce through purely computational methods.

This limitation suggests that human intelligence involves dimensions of cognition that extend beyond algorithmic processing. Embodied experience provides a context for understanding that cannot easily be reduced to data structures or symbolic reasoning.

Embodiment and Meaning

One of the most important implications of embodied intelligence concerns the nature of meaning. Human understanding emerges through interaction with environments that are experienced through the body.

Language, for example, is deeply connected to embodied experience. Words describing spatial relationships, movement, and sensation reflect how humans encounter the world physically. Even abstract concepts often originate from metaphors grounded in bodily perception.

Artificial intelligence systems can generate language that appears coherent and meaningful, yet they do not experience the embodied contexts that give language its significance. Large language models predict patterns in textual data without possessing an experiential understanding of the concepts they describe.

This distinction helps explain why AI systems sometimes produce outputs that appear plausible yet lack deeper comprehension. Without embodied experience, machines cannot anchor meaning in lived reality.

Phenomenology therefore emphasizes that understanding involves more than symbolic manipulation. Meaning arises from engagement with the world, shaped by perception, movement, and social interaction.

Embodied Intelligence in Human Practice

Embodied intelligence is visible in many aspects of human activity. Artists, athletes, musicians, and craftspeople rely heavily on forms of knowledge that cannot easily be articulated through formal rules. Their expertise develops through repeated interaction between perception and action.

In observational practices such as photography, for example, perception involves more than simply recording visual information. The observer anticipates movement, adjusts bodily orientation, and interprets environmental cues to capture meaningful moments. These processes occur through embodied awareness rather than through explicit calculation.

Scientific inquiry also involves embodied intelligence. Researchers conduct experiments, manipulate instruments, and interpret physical phenomena through sensory engagement with experimental environments. Knowledge emerges through interaction between theory, observation, and experience.

These examples illustrate how intelligence unfolds through embodied practice. Human cognition develops not only through abstract reasoning but also through lived engagement with the world.

Embodied Intelligence and Conscious Intelligence

Within the framework of Conscious Intelligence, embodiment plays a crucial role in shaping how individuals understand and guide technological systems. The CI model emphasizes three pillars—meta-awareness, interpretive agency, and responsible alignment—and embodied intelligence provides experiential grounding for each.

Meta-awareness involves reflecting on one’s own cognitive processes. Phenomenological reflection encourages individuals to examine how perception and bodily engagement influence understanding.

Interpretive agency arises from the human capacity to assign meaning to experiences. Embodied perception provides the contextual richness that allows individuals to interpret information within lived environments.

Responsible alignment involves directing technological capabilities toward ethical and constructive purposes. Embodied awareness can deepen ethical reflection by highlighting the real-world consequences of technological decisions for human experience.

By emphasizing embodiment, the CI framework reinforces the importance of human awareness in guiding artificial intelligence. Machines may extend computational capabilities, but human cognition provides the experiential perspective necessary to interpret and apply technological outputs responsibly.

Toward Embodied Artificial Intelligence

Recognizing the limitations of disembodied AI has led some researchers to explore the possibility of embodied artificial intelligence. Robotics and sensorimotor learning systems attempt to integrate perception and action within physical environments.

These approaches acknowledge that intelligence may require interaction with the world rather than purely abstract computation. Robots equipped with sensors and mobility can learn through environmental feedback, gradually developing adaptive behaviors.

While such research represents an important step toward more flexible AI systems, replicating the complexity of human embodiment remains a significant challenge. Biological organisms possess highly sophisticated sensory systems, neural architectures, and evolutionary adaptations that enable nuanced interactions with their surroundings.

Nevertheless, the exploration of embodied AI highlights an important philosophical insight: intelligence may be inseparable from the environments in which it develops.

Embodied Intelligence in a Technological Civilization

As artificial intelligence becomes increasingly integrated into modern societies, understanding embodied intelligence becomes more important than ever. Digital technologies shape how individuals perceive information, communicate with others, and interact with the world.

Yet human cognition continues to depend on embodied experience. Perception, movement, and sensory engagement remain essential components of understanding.

The rise of AI therefore does not eliminate the importance of human intelligence. Instead, it emphasizes the need for conscious awareness capable of interpreting technological systems within lived contexts.

Embodied intelligence reminds us that cognition is not simply an abstract computational function. It is an activity embedded in perception, experience, and interaction with the world.

Conclusion

The concept of embodied intelligence reveals a fundamental dimension of human cognition often overlooked in discussions of artificial intelligence. While machines excel at processing data and recognizing patterns, human intelligence arises through the dynamic interaction between mind, body, and environment.

Phenomenology provides a philosophical framework for understanding this relationship by examining the structures of lived experience. Through the work of thinkers such as Merleau-Ponty, phenomenology shows that perception and understanding emerge from embodied engagement with the world.

In the age of artificial intelligence, this perspective becomes increasingly relevant. AI systems may extend human analytical capabilities, but they remain fundamentally different from human cognition, which is grounded in embodied experience.

Within the framework of Conscious Intelligence, embodied intelligence underscores the importance of human awareness in guiding technological systems. By integrating reflection, interpretation, and responsibility, individuals can ensure that artificial intelligence serves constructive purposes within human societies.

Ultimately, understanding intelligence requires acknowledging the role of the body in shaping perception and meaning. Human awareness remains rooted in lived experience, and this experiential foundation continues to guide the evolving relationship between human cognition and artificial intelligence.

References

Dreyfus, H. L. (1992). What computers still can’t do: A critique of artificial reason. MIT Press.

Merleau-Ponty, M. (2012). Phenomenology of perception. Routledge. (Original work published 1945)

Varela, F. J., Thompson, E., & Rosch, E. (1991). The embodied mind: Cognitive science and human experience. MIT Press.



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