UX STRATEGY • PRODUCT DESIGN
Research. Strategy. Systems Design. AI.
I design products that help people understand and trust systems they can't fully see — from machine vision overlays to GPS-connected hardware. That's been my work for over two decades. AI just made it more interesting. (See how I'm integrating AI into my design process.)
Machine Vision
Wearable
AI • AGEnts
22
YEARS EXPERIENCE
3
TECHNOLOGY COMPANIES
Multiple
PATENTS FILED/GRANTED
01
Selected Work
Case Studies
Global Trend Engine
Designer & Builder
•
Self-Initiated
•
1 week
→
An agentic AI dashboard where three agents — a multi-persona scanner, Nostradamus, and Tarot — scan the web for frontier signals, synthesize patterns, and generate predictive convergence insights — designed, built, and deployed with Claude.
Lytx Video Overlay
Senior UX Designer
•
Lytx
•
4 months
→
A dynamic video overlay that simplifies customer coaching conversations, improving user trust, and reducing contention rate to near zero.
Lytx Driver ID
Senior UX Designer
•
Lytx
•
3 months
→
A systematic user flow for assignment, distribution, and usage of QR codes to assign drivers to vehicles.
Timex Ironman ONE GPS+
UX Design Lead
•
Qualcomm / Timex
•
3 years
→
A full 0-to-1 product experience for athletes who wanted to track workouts, stay connected, and leave their phones behind.
Tagg the Pet Tracker
UX Design & Product Management
•
Qualcomm
•
1.5 years
→
Redesign of a pet activity monitoring and management of iOS/Android app development as both UX lead and product manager.
FLO TV Personal Television
UX Design Lead
•
Qualcomm / FLO TV
•
1 year
→
Designing a new product category from the ground up: live mobile television.
02
Expertise
Skills
User Research
Interviews, usability tests, ethnographic study, and contextual inquiry
Journey Mapping
End-to-end experience mapping across touchpoints
Interaction Design
Flows, wireframes, use cases, and high-fidelity mockups
Storytelling
Communicating design decisions to executives and cross-functional teams
Systems Thinking
Mapping connected flows and designing for scalability across surfaces
Prototyping
From low-fi click-throughs to high-fidelity AI coding
AI Fluency
Designing AI-powered experiences and using AI tools in the design process
Stakeholder Management
Cross-functional collaboration and design advocacy
Tools
Claude (Design/Code)
Research synthesis, design, prototyping, and documentation
Figma
Components, variants, auto-layout, and prototyping
Sketch
Vector UI design for components and high-fidelity screens
Axure RP
High-fidelity prototyping with conditional logic
Pendo
In-app guidance, feature tracking, and user surveys
Amplitude
Funnels, retention, and feature-adoption analysis
Dovetail
Centralized research insights and customer intelligence
Atlassian (Confluence/Jira)
Confluence for design documentation; Jira for planning and cross-functional tracking
03
Background
Experience & Education
2019 - Present
Senior Product Designer
Lytx • San Diego, CA
Research and design of user experience for video-safety and AI products, and development of an AI-native design process for the Product/UX team.
2007 - 2018
Sr. Staff UX Designer & Sr. Product Manager
Qualcomm • San Diego, CA
Led 0-to-1 UX and product development for large-scale product start-ups while managing 3rd-party design teams.
2003 - 2007
UI Designer
Nokia • San Diego, CA
Designed new phone features while serving as the only North American member of Nokia's global design-management team.
1998 - 2002
B.S. Symbolic Systems
Stanford University • Stanford, CA
Interdisciplinary study of computer science, linguistics, philosophy, and psychology with a concentration in human-computer interaction.
My Résumé
Name
Daniel Rivas
current role
Senior Product Designer
Location
San Diego, CA
Availability
Daily Futurology Report
September 27, 2026 at 1:36:45 AM
Model:
Opus 4.8
Forecast:
2026–2028
Three labs stood up their own AI referee — then watched critics call it toothless and state AGs demand a real one, in the same week the rare-earth cliff held for Nov 10, the WMO called El Nino possibly the largest in a millennium, and Google's first orbital chips readied for an Oct 1 launch. Through 2028 the binding question isn't whether the models work — it's which referee governs them, and who it answers to.
Here's the bet through 2028: the fight was never whether the models work — it's who writes the rules and who they answer to, and this week that got harder to paper over. The labs built their own referee; the same days, the people a real one would regulate asked Congress to build that instead. When the industry is standing up a self-regulator and lobbying for a federal one at the same time, what you're watching is a hedge — grade your own homework, but keep a government option open in case the politics turn.
The evidence is on the calendar, not in the mood music. SAFA, announced late September. A coalition of state AGs writing Congress on September 23. A 417-3 House vote, dead in the Senate two days on. A rare-earth suspension that lapses November 10. A WMO El Nino it now calls possibly the largest in a thousand years, peaking near a record at year's end. NERC adding 224 gigawatts. Half a dozen clocks, all bunched into one quarter.
I'd watch October into December hardest. That's when the rare-earth cliff either lapses or gets waved off, when El Nino peaks and breaks whatever it breaks, when Google's first chips either survive orbit or don't, when the midterms turn power bills into votes, and when SAFA either signs real auditors or stays a press release with famous names on it. A lot settles in one quarter, and given the year, I wouldn't put the whole board on the calm read.
Worth holding loosely, though. A self-regulator can grow teeth — FINRA has them, sometimes — and a deadline is a negotiation, not a wall; the rare-earth freeze already slipped once. The likeliest 2028 isn't one clean cliff, it's slippage: everything a little slower, a little more gas-fired, a little more governed-by-the-governed than anyone meant to admit. And the development I keep circling back to isn't on the countdown at all — this fall an edit inside a living body just kept working, while the rest of us argued about the whistle.
Cross-Domain Synthesis
Last week three labs stood up their own referee. This week everyone got a good look at it and started arguing. Lawfare wrote up how a FINRA-for-AI might actually work; Forkast ran the history and figured it probably won't be a brake. And a coalition of state attorneys general — plus, oddly, OpenAI itself — spent the same days asking Congress for the mandatory federal rules the private body is supposed to stand in for. So the question stopped being whether anyone regulates this and became which referee, and who it answers to.
Meanwhile the physical layers kept their own schedule. China's rare-earth freeze still lapses November 10 — the Trump-Xi state visit in Washington bought a handshake, not a fix, and analysts still call it relief without resolution. NERC now sees 224 more gigawatts of peak demand this decade, 69% more than it guessed a year ago, and PJM capacity prices are up 833%. The power gets built or it doesn't, and mostly it's getting billed to households.
The bill for that landed in politics. The House voted 417 to 3 to make data centers pay their own way, the Senate killed it two days later for having no teeth, and now it's a midterm issue with November 3 bearing down. Overhead, Google's about to find out whether AI chips survive orbit — Suncatcher goes up October 1. Every one of these has a date on it, and they're all stacked into the same eight weeks.
The thing that answers to none of the calendar is biology. The FDA just took Intellia's in-body CRISPR application, a gene-edited pig kidney passed 271 days dialysis-free, and the Cleveland Clinic edit is still holding cholesterol down a year on. No cliff, no cloture vote, no summit. While everyone argues over who gets to hold the whistle, the quietest layer keeps compounding.
05
Horizon: Predictive convergence
Futurology Report — Daily
September 27, 2026 at 1:36:45 AM
AI Model:
Opus 4.8
Forecast:
2026–2028
Three labs stood up their own AI referee — then watched critics call it toothless and state AGs demand a real one, in the same week the rare-earth cliff held for Nov 10, the WMO called El Nino possibly the largest in a millennium, and Google's first orbital chips readied for an Oct 1 launch. Through 2028 the binding question isn't whether the models work — it's which referee governs them, and who it answers to.
Here's the bet through 2028: the fight was never whether the models work — it's who writes the rules and who they answer to, and this week that got harder to paper over. The labs built their own referee; the same days, the people a real one would regulate asked Congress to build that instead. When the industry is standing up a self-regulator and lobbying for a federal one at the same time, what you're watching is a hedge — grade your own homework, but keep a government option open in case the politics turn.
The evidence is on the calendar, not in the mood music. SAFA, announced late September. A coalition of state AGs writing Congress on September 23. A 417-3 House vote, dead in the Senate two days on. A rare-earth suspension that lapses November 10. A WMO El Nino it now calls possibly the largest in a thousand years, peaking near a record at year's end. NERC adding 224 gigawatts. Half a dozen clocks, all bunched into one quarter.
I'd watch October into December hardest. That's when the rare-earth cliff either lapses or gets waved off, when El Nino peaks and breaks whatever it breaks, when Google's first chips either survive orbit or don't, when the midterms turn power bills into votes, and when SAFA either signs real auditors or stays a press release with famous names on it. A lot settles in one quarter, and given the year, I wouldn't put the whole board on the calm read.
Worth holding loosely, though. A self-regulator can grow teeth — FINRA has them, sometimes — and a deadline is a negotiation, not a wall; the rare-earth freeze already slipped once. The likeliest 2028 isn't one clean cliff, it's slippage: everything a little slower, a little more gas-fired, a little more governed-by-the-governed than anyone meant to admit. And the development I keep circling back to isn't on the countdown at all — this fall an edit inside a living body just kept working, while the rest of us argued about the whistle.
Signal Intensity
A domain-level score (0-100) representing the volume and momentum of frontier activity detected across the signals in that domain.
AI
97
%
Climate
94
%
BIOTECH
66
%
GEOPOLITICS
80
%
ENERGY
95
%
SOCIETY
90
%
SPACE
63
%
Cross-Domain Synthesis
Last week three labs stood up their own referee. This week everyone got a good look at it and started arguing. Lawfare wrote up how a FINRA-for-AI might actually work; Forkast ran the history and figured it probably won't be a brake. And a coalition of state attorneys general — plus, oddly, OpenAI itself — spent the same days asking Congress for the mandatory federal rules the private body is supposed to stand in for. So the question stopped being whether anyone regulates this and became which referee, and who it answers to.
Meanwhile the physical layers kept their own schedule. China's rare-earth freeze still lapses November 10 — the Trump-Xi state visit in Washington bought a handshake, not a fix, and analysts still call it relief without resolution. NERC now sees 224 more gigawatts of peak demand this decade, 69% more than it guessed a year ago, and PJM capacity prices are up 833%. The power gets built or it doesn't, and mostly it's getting billed to households.
The bill for that landed in politics. The House voted 417 to 3 to make data centers pay their own way, the Senate killed it two days later for having no teeth, and now it's a midterm issue with November 3 bearing down. Overhead, Google's about to find out whether AI chips survive orbit — Suncatcher goes up October 1. Every one of these has a date on it, and they're all stacked into the same eight weeks.
The thing that answers to none of the calendar is biology. The FDA just took Intellia's in-body CRISPR application, a gene-edited pig kidney passed 271 days dialysis-free, and the Cleveland Clinic edit is still holding cholesterol down a year on. No cliff, no cloture vote, no summit. While everyone argues over who gets to hold the whistle, the quietest layer keeps compounding.
ABOUT ME
Design Philosophy
I bring order to complexity; I've spent over two decades building the tools to do it well.
My path started at Stanford, where a degree in Symbolic Systems gave me something most designers don't have: a foundation that spans both sides of the human-computer divide. From the technical rigor of computer science and formal logic, to the human depth of cognitive psychology and knowledge representation, I learned to hold both perspectives at once and to design from the intersection.
That training became practice at Nokia, Qualcomm, and now Lytx, companies where the problems are large, the systems are complex, and the stakes are real. I've learned that the most important design decisions rarely live on a single screen. They live in the architecture, the mental models, the moments where a user either trusts the product or doesn't.
What drives me today is the challenge of making emerging technology feel human and trustworthy. AI systems can process the world faster than any person, but they still need to communicate their reasoning, surface the right information at the right moment, and earn the confidence of the people who depend on them. That translation problem, from machine intelligence to human understanding, is exactly the kind of complexity I've been working on.
Systems before screens
Every interface is a surface on top of a system. Understanding the system (the data flows, the user mental models, the organizational constraints) is what separates design that scales from design that just looks good in a mockup.
Strategy and execution, not one or the other
I connect design decisions to business outcomes. That means being in the room when strategy is set, not just when wireframes need approval. It means being able to move between the 30,000-foot view and the pixel-level detail without losing either.
Trustworthy by design
The best technology earns trust before it demands it. Whether I'm designing a safety-critical AI product or the home screen on a fitness watch, I start with the question: what does this person need to feel confident taking action?
EDUCATION
B.S. Symbolic Systems
Stanford University
Concentration in HCI
BASED IN
San Diego, CA
CURRENTLY
Senior Product Designer
Lytx
OPEN TO OPPORTUNITIES
Principal/Senior-Level Product Design Roles
In San Diego or Remote
SELECTED WORK
Case Studies
EXPERTISE
Skills
User Research
Interviews, usability tests, ethnographic study, and contextual inquiry
Journey Mapping
End-to-end experience mapping across touchpoints
Interaction Design
Flows, wireframes, use cases, and high-fidelity mockups
Storytelling
Communicating design decisions to executives, stakeholders, and cross-functional teams
Systems Thinking
Mapping connected flows and designing for scalability across product surfaces
Prototyping
Interactive prototypes from low-fi click-throughs to high-fidelity AI coding
AI Fluency
Designing AI-powered experiences and leveraging AI tools in the design process
Stakeholder Management
Cross-functional collaboration and design advocacy
Tools
Pendo
In-app guidance, feature tracking, and user surveys to inform design decisions
Dovetail
Centralized research insights, tagged findings, and shared customer intelligence repository
Figma
Components, variants, auto-layout, and prototyping
Claude (Design & Code)
AI-assisted research synthesis, design critique, content generation, and prototyping
Amplitude
Product funnels, retention curves, and feature adoption to identify usage and priorities
Gong
Customer interview repository for surfacing pain points, testing designs, and grounding decisions
Sketch
Vector-based UI design for components, wireframes, and high-fidelity screens
Axure RP
High-fidelity interactive prototyping with conditional logic and complex flows
Cursor
AI-assisted coding for rapid prototyping and exploring technical feasibility
BACKGROUND
Experience & Education
Senior Product Designer
Lytx · San Diego, CA
Researched and designed user experience for video safety and AI products, and developed an AI-native design process for the UX team.
tagsContainer
2019 - Present
Sr. Staff UX Designer & Sr. Product Manager
Qualcomm · San Diego, CA
Led 0-to-1 UX and product development for large-scale product start-ups while managing 3rd-party design teams.
tagsContainer
2007 - 2018
UI Designer
Nokia · San Diego, CA
Designed new phone features while serving as the only North American member of Nokia's global design management team.
tagsContainer
2003 - 2007
B.S. Symbolic Systems
Completed interdisciplinary study of computer science, linguistics, philosophy, and psychology with a concentration in human-computer interaction.
tagsContainer
1998 - 2002
My Résumé
A full overview of my experience, skills, and education — ready to share.
NAME
Daniel Rivas
CURRENT ROLE
Senior Product Designer
LOCATION
San Diego, CA
EXPERIENCE
22 years
AVAILABILITY
✦ Human · Machine · Intelligence ✦ Systems before screens ✦ Strategy & execution ✦ Trustworthy by design ✦
Design Philosophy
06
ABOUT ME
I bring order to complexity; I've spent over two decades building the tools to do it well.
My path started at Stanford, where a degree in Symbolic Systems gave me something most designers don't have: a foundation that spans both sides of the human-computer divide. From the technical rigor of computer science and formal logic, to the human depth of cognitive psychology and knowledge representation, I learned to hold both perspectives at once and to design from the intersection.
That training became practice at Nokia, Qualcomm, and now Lytx, companies where the problems are large, the systems are complex, and the stakes are real. I've learned that the most important design decisions rarely live on a single screen. They live in the architecture, the mental models, the moments where a user either trusts the product or doesn't.
What drives me today is the challenge of making emerging technology feel human and trustworthy. AI systems can process the world faster than any person, but they still need to communicate their reasoning, surface the right information at the right moment, and earn the confidence of the people who depend on them. That translation problem, from machine intelligence to human understanding, is exactly the kind of complexity I've been working on.
The surface
The Model
Every interface is a surface on top of a system.
The screen is the visible tip. The decisions that make a product trustworthy live underneath it — in the flows, the mental models, the constraints. That's where I start.
Screen / Interface
What the user sees
User mental models & needs
01
Interaction & info architecture
02
Data flows & system states
03
Organizational constraints
04
↓ Where the design decisions live
A
Systems before screens
Every interface is a surface on top of a system. Understanding the data flows, mental models, and organizational constraints is what separates design that scales from design that just looks good in a mockup.
B
Strategy and execution
I connect design decisions to business outcomes — being in the room when strategy is set, moving between the 30,000-foot view and the pixel-level detail without losing either.
C
Trustworthy by design
The best technology earns trust before it demands it. Whether a safety-critical AI product or a fitness-watch home screen, I start with: what does this person need to feel confident taking action?
Education
B.S. Symbolic Systems
Stanford University • Concentration in HCI
Based In
San Diego, CA
Available for remote
Currently
Senior Product Designer
Lytx, Inc.
Open To
Principal / Lead / Senior roles
San Diego or remote
07
Contact
Have a project in mind?
I'm open to new full-time opportunities, collaborations, and interesting conversations.
Phone
Daniel Rivas · UX Strategy & Product Design
© 2026