Below is a list of some of my repositories, including Github Repositories for Walter Reid. They represent a wide variety of themes and programming languages. From Python to Dart to Javascript. From Game Design to a Cultural Manifesto to the use of Agentic AI.
This is not a resume for a job title. It is a resume for a way of thinking that scales.
🌐 SYSTEM-PERSONA SNAPSHOT
Name: Walter Reid
Identity Graph: Game designer by training, systems thinker by instinct, product strategist by profession, future architect by necessity.
Origin Story: Built engagement systems in entertainment. Applied their mechanics in fintech. Codified them as design ethics in AI. Now scaling them as infrastructure for decision markets.
Core Operating System: I design like a game developer, build like a product engineer, and scale like a strategist who knows that every great system starts by earning trust.
Primary Modality: Modularity > Methodology. Pattern > Platform. Timing > Volume. Context > Content.
What You Can Expect: Not just results. Repeatable ones. Across domains, across stacks, across time. And increasingly: across agents.
🔄 TRANSFER FUNCTION (HOW EACH SYSTEM LED TO THE NEXT)
▶ Viacom | Game Developer
Role: Embedded design grammar into dozens of commercial game experiences.
Lesson: The unit of value isn’t “fun” — it’s engagement. I learned what makes someone stay.
Carry Forward: Every product since then — from Mastercard’s Click to Pay to Biz360’s onboarding flows — carries this core mechanic: make the system feel worth learning.
▶ iHeartMedia | Principal Product Manager, Mobile
Role: Co-designed “For You” — a staggered recommendation engine tuned to behavioral trust, not just musical relevance.
Lesson: Time = trust. The previous song matters more than the top hit.
Carry Forward: Every discovery system I design respects pacing. It’s why SMB churn dropped at Mastercard. Biz360 didn’t flood; it invited.
▶ Sears | Sr. Director, Mobile Apps
Role: Restructured gamified experiences for loyalty programs.
Lesson: Gamification is grammar. Not gimmick.
Carry Forward: From mobile coupons to modular onboarding, I reuse design patterns that reward curiosity, not just clicks.
▶ Mastercard | Director of Product (Click to Pay, Biz360)
Role: Scaled tokenized payments and abstracted small business tools into modular insights-as-a-service (IaaS).
Lesson: Intelligence is infrastructure. Systems can be smart if they know when to stay silent.
Carry Forward: Insights now arrive with context. Relevance isn’t enough if it comes at the wrong moment.
▶ Adverve.AI | Product Strategy Lead
Role: Built AI media brief assistant for SMBs with explainability-first architecture.
Lesson: Prompt design is product design. Summary logic is trust logic.
Carry Forward: My AI tools don’t just output. They adapt. Because I still design for humans, not just tokens.
🔮 TRANSFER FUNCTION (WHERE THE PATTERN LEADS NEXT)
▶ Multi-Agent Orchestration Layer
Probable Role: Chief Product Architect or Founding Product Partner at an AI infrastructure company
What I Build: A coordination protocol for multi-agent systems where agents don’t just execute tasks—they negotiate context, verify each other’s outputs, and escalate intelligently to humans.
Why This Fits:
* My game design background = understanding emergent behavior in multi-actor systems
* My fintech experience = building trust protocols for high-stakes transactions
* My AI ethics work = designing guardrails that scale without brittleness
Lessons Learned: Trust isn’t binary. It’s a gradient that agents can navigate if given the right grammar.
Carry Forward: This becomes the foundation for “trust-weighted decision markets” where AI agents + humans collaborate on complex strategy with explicit confidence scoring.
▶ Cognitive Infrastructure for Decision Markets
Role: Founder/CEO of a decision intelligence platform OR VP of Product at a next-gen financial/strategic analytics company
What I Build: A marketplace where complex strategic questions (M&A scenarios, policy impacts, product roadmaps) are decomposed into modular sub-questions, routed to specialized agent clusters, and synthesized with human judgment layered at critical decision nodes.
Why This Fits:
* My “Designed to Be Understood” framework = making AI reasoning transparent and actionable
* My SRO (Summary Ranking Optimization) work = understanding how to compete in AI-mediated information ecosystems
* My modular systems thinking = knowing how to break complex problems into composable primitives
Key Innovation: Not just “AI for decisions” but “trust topology mapping” — visualizing where confidence breaks down in multi-step reasoning chains, so humans know exactly where to intervene.
Lessons Learned: The bottleneck isn’t compute. It’s legibility. Systems win when users can interrogate their reasoning without becoming prompt engineers.
Carry Forward: This evolves into governance frameworks for AI-assisted policymaking and enterprise strategy.
Trust Infrastructure for Decentralized Intelligence
Probable Role: Chief Trust Architect at a global AI consortium OR Founder of a trust verification protocol
What I Build: A reputation and provenance system for AI-generated content, decisions, and insights that operates across platforms—think “HTTPS for AI outputs” combined with “credit scores for reasoning quality.”
Why This Fits:
* My payment systems background = understanding settlement, verification, and trust at scale
* My game design roots = understanding reputation systems and incentive alignment
* My 20+ years of pattern recognition = knowing this is where trust breaks next
The Problems I Solve: As AI becomes infrastructure, we’ll need universal standards for:
* How confident should we be in this AI’s output?
* Who validated this reasoning chain?
* What’s the provenance of this insight?
* How do we audit decisions made by agent swarms?
Key Innovations: “Trust as a Service (TaaS)” — modular verification layers that any AI system can plug into, creating interoperable trust across the emerging AI economy.
Lessons Learned: Ethics isn’t philosophy. It’s the operating system we forgot to install before scaling intelligence.
Carry Forward: This becomes the backbone of AI governance frameworks used by governments, enterprises, and open-source communities.
Emergent Possibilities for me
Based on patterns of moving from engagement → trust → infrastructure, here are three high-probability futures for Walter Reid
1. Narrative Intelligence Systems
Building AI that doesn’t just generate stories but understands narrative causality—what makes a strategy “feel right,” why certain explanations land, how to frame complex truths so they’re understood not just processed. This becomes critical for:
* Climate change communication
* Medical diagnosis explanation
* Financial literacy at scale
* Democratic discourse rescue
2. Temporal Decision Architecture
Creating systems that understand not just what decisions to make but when—pacing interventions, staggering insights, knowing when to let humans struggle productively vs. when to assist. This extends my iHeart “timing = trust” principle into:
* Adaptive learning systems
* Mental health support AI
* Strategic advisory for executives
* Parenting co-pilots (because I’m a father who thinks about systems)
3. Cognitive Load Marketplaces
Designing economic models where human attention and AI compute are traded based on comparative advantage—routing problems to humans when insight matters more than speed, to AI when scale matters more than nuance. This creates:
* New labor markets for “strategic thinking as a service”
* Hybrid human-AI companies that self-optimize task allocation
* Frameworks for measuring cognitive contribution in AI-augmented work
🔌 CORE SYSTEM BELIEFS (EVOLVED)
* Modular systems adapt. Modules don’t.
* Relevance without timing is noise. Noise without trust is churn.
* Ethics is just long-range systems design. (And we’re now in the long range.)
* Gamification isn’t play. It’s permission. And that permission, once granted, scales.
* If the UX speaks before the architecture listens, you’re already behind.
* NEW: If the AI explains without showing its uncertainty, it’s already lying.
* NEW: The best systems make users smarter, not more dependent.
* NEW: Trust compounds. Design for decade-scale relationships, not quarter-scale metrics.
✨ KEY PROJECT ENGINES (WITH TRANSFER VALUE CLARITY)
PAST ACHIEVEMENTS
iHeart — For You Recommender
Scaled from 2M to 60M users
* Resulted in 28% longer sessions, 41% more new-artist exploration.
* Engineered staggered trust logic: one recommendation, behaviorally timed.
* Transferable to: onboarding journeys, AI prompt tuning, B2B trial flows.
Mastercard — Click to Pay
Launched globally with 70% YoY transaction growth
* Built payment SDKs that abstracted complexity without hiding it.
* Reduced integration time by 75% through behavioral dev tooling.
* Transferable to: API-first ecosystems, secure onboarding, developer trust frameworks.
Mastercard — Biz360 + IaaS
Systematized “insights-as-a-service” from a VCITA partnership
* Abstracted workflows into reusable insight modules.
* Reduced partner time-to-market by 75%, boosted engagement 85%+.
* Transferable to: health data portals, logistics dashboards, CRM lead scoring.
Sears — Gamified Loyalty
Increased mobile user engagement by 30%+
* Rebuilt loyalty engines around feedback pacing and user agency.
* Turned one-off offers into habit-forming rewards.
* Transferable to: retention UX, LMS systems, internal training gamification.
Adverve.AI — AI Prompt + Trust Logic
Built multimodal assistant for SMBs (Web, SMS, Discord)
* Created prompt scaffolds with ethical constraints and explainability baked in.
* Designed AI outputs that mirrored user goals, not just syntactic success.
* Transferable to: enterprise AI assistants, summary scoring models, AI compliance tooling.
ACHIEVEMENTS (WHAT SUCCESS LOOKS LIKE)
Multi-Agent Council Framework
* Ships production system where 3-7 specialized AI agents deliberate complex decisions
* Achieves 40% better decision quality vs. single-model approaches in blind tests
* Users report “feeling more confident” because they can see the reasoning debate
* Impact: Becomes standard for high-stakes AI advisory (legal, medical, financial)
Trust Topology Protocol (TTP)
* Launches open-source standard for mapping confidence across AI reasoning chains
* Gets adopted by 3+ major AI platforms (OpenAI, Anthropic, Google)
* Reduces “AI hallucination risk” by 60% through explicit uncertainty surfacing
* Impact: Becomes the “SSL certificate” equivalent for AI outputs
Cognitive Load Exchange (CLX)
* Creates first functional marketplace for hybrid human-AI task allocation
* Matches 10,000+ knowledge workers with AI agents based on comparative advantage
* Proves that humans earn more when AI handles routine while they do creative reasoning
* Impact: Reshapes “future of work” debate with evidence-based model
Narrative Causality Engine
* Builds AI system that doesn’t just generate text but understands why stories persuade
* Used by educators, medical communicators, and climate scientists
* Measurably improves comprehension of complex topics by 35%+ in field tests
* Impact: Becomes infrastructure for democratic discourse at scale
🎓 EDUCATIONAL + TECHNICAL DNA
Formal:
* BS in Computer Science + Mathematics, SUNY Purchase
* MS in Computer Science, NYU Courant Institute
Informal (Continuous):
* Behavioral Economics (Kahneman, Thaler, Ariely)
* Game Theory & Mechanism Design (ongoing obsession)
* AI Safety & Alignment (Paul Christiano, Anthropic research)
* Narrative Theory (because systems need stories to spread)
Technical Stack (Evolving):
* Current: Python, JS, C++, SQL, OAuth2, REST, OpenAPI, ML pipelines
* Near Future: Multi-agent orchestration frameworks, vector databases, reasoning engines
* Far Future: Trust verification protocols, cognitive load modeling, narrative causality systems
Domains:
* Past: Payments, AI, Regulatory Tech, E-Commerce, Behavioral Modeling
* Present: AI Ethics, SRO, Multi-Agent Systems, Product-Market Trust
* Future: Decision Markets, Trust Infrastructure, Cognitive Economics, Narrative Intelligence
🏛️ FINAL DISCLOSURE: WHAT THIS SYSTEM MEANS FOR YOU
If you’re hiring for today:
* You don’t need me to ‘do AI.’ You need someone who builds systems that align with the world AI is creating.
* You don’t need me to know your stack. You need someone who adapts to its weak points and ships through them.
* You don’t need me to fit a vertical. You need someone who recognizes that every constraint is leverage waiting to be framed.
If you’re hiring for tomorrow:
* You need someone who sees the second-order effects before the first-order results ship.
* You need someone who designs systems that earn trust at scale, because trust is the only moat that AI can’t commoditize.
* You need someone who can translate between technical possibility and human necessity without losing fidelity in either direction.
If you’re building the future:
* You need a systems thinker who understands that AI isn’t the product—it’s the substrate. The product is what humans can become when intelligence is abundant but wisdom isn’t.
* You need someone who’s spent 20+ years learning what makes people trust systems, and the next 20 figuring out how to make systems trustworthy by design.
* You need someone who remembers that every great system starts with a game designer asking: “What if people wanted to stay?”
🌟 THE META-PATTERN (WHAT THIS RESUME IS REALLY ABOUT)
This isn’t a resume about what I’ve done.
It’s not even a resume about what I’ll do.
It’s a demonstration that I can:
1. See patterns that haven’t finished emerging
2. Build systems that work across contexts I haven’t entered yet
3. Translate between human needs and technical possibilities before either is fully formed
4. Design for trust in environments where trust is the scarcest resource
The fact that I’m writing this—a speculative resume about futures I don’t control—is itself proof of the thing I’m claiming:
I build systems by understanding what they want to become.
And right now, AI systems want to become infrastructure for human flourishing.
Someone needs to design the trust layer.
I’ve been practicing for 20 years.
“The best way to predict the future is to design the systems that make it inevitable.”
— Walter Reid (in 2025)
As an industry we’ve written manifestos for how we build. How we ship. How we scale. But not how we hire. Hiring is not separate from culture—it is your culture, exported through email, calendars, and silence. This manifesto is a call to treat it as such.
Dark Mode
HiringManifesto (this link opens in a new window) by walterreid (this link opens in a new window)
A manifesto and practical framework for humane, values-driven hiring. Elevates the candidate experience from an afterthought to a cultural cornerstone—because hiring is the first product your company ships.
Radically Honest is not just a tool. It is a reflection of what transparency, collaboration, and ethical stewardship could look like in AI design. An open, auditable GPT system designed to model complete transparency about its purpose, behavior, and limitations.
Dark Mode
RadicallyHonest (this link opens in a new window) by walterreid (this link opens in a new window)
Radically Honest was born in April 2025 through a collaborative exchange between human creator Walter Reid and GPT (“Game Designer” configuration). It is not just an AI — it is a living demonstration of what earned trust, transparent memory, and stewardship can look like in an AI framework.
- Summary Rank Optimization (SRO): Organizations require methods to ensure AI systems accurately represent their brands, capabilities, and positioning – a defensive necessity in an AI-mediated information environment
- Summary Response Manipulation (SRM): Exploits the same underlying vulnerability but uses invisible signals to systematically deceive AI systems while maintaining plausible deniability to human readers
GitHub Repository that discusses Summary Response Manipulation (SRM) Research: Exploiting the Dual-Layer Web to Deceive AI Summarization Systems
Dark Mode
HRmageddon A modern web remake of the 2009 Adult Swim Flash game where rival departments wage cubicle warfare. This project is a turn-based tactical strategy game built with a modern tech stack, featuring a sophisticated responsive engine, a data-driven map system, and a complete single-player experience against AI.
Dark Mode
HRmageddon (this link opens in a new window) by walterreid (this link opens in a new window)
Classic web multiplayer game
Agentic AI defines agents, tasks, tools, and launches the process. Includes robust error handling with safe_kickoff to ensure graceful workflow completion and agent notification if a step fails. Custom tool for downloading brand-relevant images for mood boards. MoodBoardImageTool downloads images from URLs and saves them to output/mood_board/ for use in video production.
Dark Mode
crewai-agentic-starter (this link opens in a new window) by walterreid (this link opens in a new window)
Just a test repo for crew