Ode by Muno.
āReverse-engineering the thinking behind what you experienceā
Understanding How Computational Perception Shapes Intelligence
I'm exploring a question that sits at the intersection of AI and human experience: How do we build systems that understand perception the way humans do?
Why does a machine learning model struggle to evaluate what makes one restaurant memorable and another forgettable? Why can't traditional data capture the difference between a luxury brand that feels inevitable and one that feels generic? Why do the most valuable trend signals often come from sensory and experiential data that conventional analysis ignores?
The answer lies in computational perception: teaching systems to process the multisensory information that shapes how we understand quality, authenticity, and value.
I come from an engineering and AI background: systems thinking, pattern recognition, building tools for predictive insights. Now I'm applying that lens to decode how perception works across eight interconnected systems, and what that reveals about markets, technology, and human behavior.
What This Is
There are formal disciplines for studying perception. Neuroscience maps how the brain processes sensory input. Computer vision teaches machines to see. Olfactory AI is learning to smell. Haptic engineering gives robots touch. Audio processing decodes sound.
But most people building AI tools don't study these systematically. They build models, optimize algorithms, analyze traditional datasets. And yet the most interesting problems to me: evaluating brands, predicting consumer behavior, understanding cultural trends, require processing the same multisensory information humans use instinctively.
This is the gap Ode explores: perception as data, multisensory intelligence as competitive advantage.
Whether you're building tools, evaluating companies, designing products, or trying to understand why certain experiences create lasting value, you're working with perceptual systems whether you realize it or not.
Ode is my research log for making that explicit. Documenting frameworks for computational perception that work across domains.
How Ode Works
Ode operates across integrated arms :
Research & Frameworks: Systematic analysis of how perceptual systems create value from sensory AI market opportunities to cultural trend prediction.
Tools & Products: Proof-of-concept tools that systematize perceptual data for strategic decision-making.
Experiences: Curated events that translate frameworks into practice: fragrance workshops, restaurant deconstructions, tasting laboratories where computational perception becomes embodied learning.
The research informs the tools. The tools get tested through experiences. Together, they prove that perception is a system you can reverse-engineer, model, and apply.
The 8 Senses
Perception isn't random. It's organized around eight interconnected senses:
The Five Classical Senses:
Taste ā Culinary structure, flavor dependencies, gastronomic literacy
Scent ā Fragrance architecture, olfactory memory, molecular storytelling
Touch ā Texture economics, material intimacy, tactile design
Sight ā Visual composition, aesthetic infrastructure, design as language
Sound ā Acoustic environments, sonic narrative, auditory mood design
Three Engineering Extensions:
Space ā How environments structure behavior, urban UX, architectural influence
Time ā Sequence as narrative, temporal pacing, experience design over duration
System ā Pattern recognition, interdependencies, how small elements create emergence
Each sense is a data stream. Together, they form a framework for understanding how humans (and increasingly, machines) process information and make decisions.
This is how I analyze everything: consumer brands, restaurant economics, AI capabilities, investment opportunities. If you can decode the perceptual systems at work, you can predict what creates value and what doesn't.
Why 8 sensesā¦ not 5?
My go-to explanation is: "Majorly because of my engineering background, but also because the five senses can't explain why you remember a restaurant."
Think about it: you walk into a place and immediately know whether you want to stay or leave before you've tasted anything, sometimes before you've even looked at the menu.
That's not taste, scent, touch, sight, or sound working alone. That's space: ceiling height, table spacing, how the room makes you feel small or expansive.
Then the meal unfolds over two hours. The pacing between courses (when something arrives, how long you wait) completely changes the experience. A dish served too fast loses impact. The same dish with perfect timing becomes memorable.
That's not one of the five senses either. That's time.
And somehow, your brain is tracking all of this: noticing patterns, making connections, sensing when something feels coordinated versus chaotic. When the lighting, music, scent, and service all align, you feel it. When they're off by even a little, you feel that too.
That's system, your ability to sense whether all the pieces fit together or not.
The five classical senses tell you what you're experiencing. Space, time, and system tell you how it all connects.
That's why a Michelin-starred restaurant isn't just about the food. It's about orchestrating all eight. And why some places with mediocre food feel amazing: they've mastered space and time and system, even if taste isn't perfect.
My Background
I spent nearly a decade inside machine learning and AI systems: UCSD (Electrical & Computer Engineering; Revelle College), Carnegie Mellon (AI & Innovation), plus humanities, cognitive science and design because I wanted to understand not just how systems work, but how people work.
I've built AI tools for strategic insights. Evaluated startups at the intersection of technology and human experience. Worked with teams decoding the world as patterns, signals, dependencies most never notice.
What struck me was the gap: We're building increasingly sophisticated AI, but most models can't process the perceptual information that drives human decisions. They can't evaluate aesthetic quality. They can't decode why one space feels welcoming and another sterile. They can't predict which cultural trends will compound.
That's the problem space I'm working in. Not just building better algorithms, but understanding what perception actually is: how it works in humans, how it could work in machines, and what that reveals about value creation across domains.
Ode grew out of that tension. The recognition that the intelligence that builds systems can also decode experience. That computational thinking and perceptual analysis aren't opposites, they're complementary ways of understanding how the world works.
Why Ode
The name carries a double meaning. Ode is the prefix of my name, Odemuno, meaning "name" in my language. And an ode is a structured form of sustained attention; the practice of observing something closely enough to reveal its underlying architecture.
This space is both: a personal research practice and a demonstration of methodology. Showing how systematic attention to perception creates frameworks that work across technology, design, and culture.
Current Work
Right now Iām:
Publishing weekly research on how multisensory systems create connection, engagement, and memorability
Developing tools that help people explore their perceptual preferences systematically across multiple sensory dimensions
Working with early-stage founders on positioning and business models
Formalizing these frameworks through product development and curated experiences
Documenting what I learn publicly
In three months, I reached 2,000+ followers and created content that resonated deeply (40K+ views on a single piece). Because I'm demonstrating a methodology for pattern recognition that works across disciplines and people recognize the value immediately.
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Odemuno O.
Founder, Ode by Muno
Reverse-engineering the thinking behind what you experience