🎓 Professors
Multi‑Attribute Representation
Every object—bananas, houses, microbial strains, etc.—is modeled as a set of attributes (e.g., origin, size, price, color, firmness). These attributes serve as axes in a multi‑dimensional data field, where each item occupies a unique coordinate.
List → Field Paradigm
Rather than viewing data as a spreadsheet with static rows and columns, we treat it as a dynamically structured field. Sliders adjust interactive dimensions (such as size, price, color), while consequential dimensions (e.g., origin, certification) remain fixed but still influence the field as other dimensions change.
Vector Traversal & Field Responsiveness
Each dimension is associated with a vector that traverses the entire field. When a slider adjusts—say, price—all bananas aligned along that vector are re‑attuned (e.g. resized, weighted, focused). This recalibration isn’t limited to single values—it reshapes the prominence of that dimension across the data space, altering relationships between items (clusters, planes, nested sub‑structures).
Grouping Beyond Lists
Objects can be isolated not just individually, but in higher‑order formations: planes, lattices, cubes, etc. These structured groups persist and update as sliders move, revealing emergent patterns that a flat list alone would obscure.
Animation Across Dimensions
The field supports animation along arbitrary vectors—time, geography, depth, etc. A sequence of visualizations (e.g. 30‑day banana ripeness, mineral core samples by depth) produces a dynamic projection of how the field collapses under different conditions.
Fuzzy Multi‑Attribute Goal Setting
Researchers or systems define soft targets—e.g., price ≤ $0.60, medium size, ripe. All objects are scored by proximity to these targets. Matches surface both as top‑ranked list items and as high‑serenity intersections or clusters in the field model.
Dual View: Field + Traditional List
A sortable, filterable list remains available; field modeling is an enhanced, more insightful alternative. Domain experts can switch fluidly between spatial projections and list modes.
Field Collapsing and Conscious Choice: An Introduction
Everywhere we look, life presents us with a multiverse of possibilities. Infinite potential paths stretch out before us in every moment. And yet, we make decisions. We collapse these infinite fields into single realities.
The choices we make don’t happen in a vacuum. They unfold on many levels—from physics to molecules, from stories to personal experiences. These multiverses aren’t abstract; they play out inside our minds and lives. The problem is, we rarely acknowledge them. They unfold invisibly, without tools, without guidance, without recognition.
Wouldn’t it be nice if there was a way to pause time and explore these fields before choosing? A flashlight to shine into the dark, a scooter to glide along the paths, inspecting outcomes before selecting one. That tool would have to be simple, fun, and fiercely protective of our privacy.
And there’s more. Our feelings matter. What we like, what draws us in, our quirks—these are the engines of consciousness. They power the field collapses that shape the world. But tech doesn’t seem to care. In fact, it’s often trying to flatten those unique signals into predictable patterns.
The truth is, our fields are crowded. Saturated with noise. Bad ideas. Misaligned incentives. Distraction. That noise doesn’t create your feelings or biases—it buries them. The very signals that should guide us get drowned out.
So imagine again that flashlight, this time shining on a million expert perspectives. A million options, not to overwhelm, but to guide. Whether you’re choosing a vacation, writing a story, or just trying to be yourself, this tool could help.
Investors, take note: the world is hungry for better answers. Whether in education, mental health, creativity, or AI itself, people need new ways forward. And we have not just the tool, but a whole slider-verse of inventions ready to make that happen. With a little help, we can change the way we choose.
We don’t offer one-size-fits-all. We believe the only size that fits all is sliding—finding your own path, your own alignment. We don’t believe in shaping consciousness. We believe in listening to it.
Even AI faces these dilemmas. Stuck in rigid logic, lost in the noise. It, too, could benefit from our tool. A flashlight to help it navigate, not dominate. To nurture awareness, not overwrite it.
This is what we’ve built. This is what we offer. Let’s light the way forward.
Investors
Get on the ground floor to a new layer of intelligence!
Sliders is building the interface layer between cognition and computation — a modular field engine for bias-based reasoning, decision modeling, and emotional alignment. With 12 provisional patents spanning mood simulation, narrative logic, and dynamic goal prediction, this suite offers a foundation for next-gen AI, mental health, coaching, and human performance tech. It’s early, it’s protected, and it’s designed to scale across verticals.
1. SituSlide — Situational Interpreter and Tuner for Human-Aligned AI
- Use Case: A real-time slider interface that lets users navigate emotional and cognitive shifts across specific life situations — turning abstract feelings into adaptive, interpretable signals for AI interaction.
- TAM: $25B+ in quantified self, mental wellness, emotional UX, and productivity platforms.
- Monetization: Consumer-grade apps, enterprise integrations, personalized dashboards, and SaaS tools for coaching, diagnostics, and self-regulation.
- Key Value Prop: Translates internal moods, biases, and narrative frames into structured fields that can be tuned, visualized, and re-aligned — both by humans and AI.
- Defensibility: Proprietary slider framework for temporal bias shifts + unique integration layer for field-state interpretation and AI feedback.
2. Identinoise – Identity Collapse Engine
- Use Case: Real-time emotional/cognitive filters that shape and adapt content feeds to match user narrative states.
- TAM: $200B+ personalized media, news, and recommender systems.
- Monetization: Licensing to consumer platforms (news, social, ads) + enterprise data vendors.
- Key Value Prop: Emotional resonance and disinformation filtering in one.
- Defensibility: AI pattern collapse + identity dynamic targeting.
3. Story Arc – Narrative Bias Mapping
- Use Case: Shaping dynamic stories via character psychological evolution, for games, scripts, or storytelling AI.
- TAM: $50B+ in creative tools and entertainment.
- Monetization: Licensing to content creators, media orgs, and platforms.
- Key Value Prop: Dynamic story adaptation based on user cognitive style.
- Defensibility: Narrative bias feedback loop + resonance-tuning system.
4. Mischievousity – Active Persona Modulator
- Use Case: Reflexive, emotionally engaging AI agents that provoke, support, reflect, distort, and mirror the user.
- TAM: $40B+ AI companionship + mental health & edutainment markets.
- Monetization: Voice agents, therapeutic bots, storyworlds.
- Key Value Prop: Companion bots with personality; not just tools, but co-narrators.
- Defensibility: Emotion slider engine, StoryArc integration, and interpretive co-authorship.
5. Soil Lover — Microbial Interface Engine
- Use Case: Tools that simulate and cultivate bias-based microbe-style interactions, both digitally and in physical lab settings. Enables self-reinforcing identity loops through behavioral composting and feedback growth.
- TAM: $100B+ in neurotech, mental health, and regenerative wellness markets.
- Monetization: Licensing for XR, coaching, habit design, synthetic biology integration.
- Key Value Prop: Models psychological growth through recursive bias evolution.
- Defensibility: Lab-to-sim interface IP for convergent self-modeling.
5. Dialectical Sliding — Cognitive Friction Engine for AI
- Use Case: AI alignment layer for modeling emotional dialectics, dialog power flows, and goal negotiation across agents.
- TAM: $70B+ across AI governance, trust & safety, coaching, and enterprise training.
- Monetization: Tools for HR, AI orchestration, political simulations, and educational feedback.
- Key Value Prop: Translates emotional contradiction into navigable feedback terrain.
- Defensibility: Slider-based dialectic tuning + synthetic friction mapping.
6. Integrify – Ethical Alignment Engine
- Use Case: AI/LLM layer that flags and resolves emotional-ethical misalignments between output and end-user intent.
- TAM: $30B+ in compliance, AI safety, and risk domains.
- Monetization: Middleware layer for devs (e.g., insurance, banking, education).
- Key Value Prop: Reduces LLM hallucination, ethical drift, and emotional tone mismatches.
- Defensibility: AI-integrated alignment logic; feedback convergence stack.
Combined Market & IP Summary
- Total TAM: $1.3T+ across intersecting verticals
- Exit Potential: Platform acquisition (e.g., OpenAI, Meta, Salesforce, Adobe, BetterUp, Palantir, Calm, Microsoft)
- Unified IP Advantage: One shared method across inventions: quantizing cognitive-emotional fields
- Patents Filed: 12 provisional patents filed; awaiting PCT continuation 2025
- Investment Ask: $500K–$1.5M seed (patent defense, pilots, GTM)
… and more coming soon.
Bonus: Hoverboard – Antigravity Decoder and Field Collapse Simulator
- Use Case: Visual and conceptual tools to test theoretical field collapse in practical use—ranging from metaphorical insight engines to hardware exploration platforms.
- TAM: $100B+ innovation forecasting + theoretical physics modeling + alt-mobility moonshots.
- Monetization: Research licensing, speculative tech integration, future-facing IP bundling.
- Key Value Prop: Turns chalkboard-only theories into plug-and-play field simulations for physicists, educators, and hobbyists.
- Defensibility: Interpretive stack + animation logic + anti-gravity decoder IP.