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 26, 2026 at 1:36:53 AM
Model:
Opus 4.8
Forecast:
2026–2028
The three companies that build the frontier stood up their own regulator (SAFA) instead of waiting for one — the same week the Senate killed the 417-3 ratepayer bill, the rare-earth cliff held for Nov 10, and a near-100% El Nino aimed past every modern record. Through 2028 the binding constraint is who writes the rules, not whether the models can.
Here's the bet through 2028: the fight was never about whether the models work — it's about who gets to write the rules, and this week that fight got more honest. The three companies building the frontier stopped waiting for a government referee and stood one up themselves. Call it what it is — the industry would rather grade its own homework than let anyone else hold the pen. When the people with the most to lose from oversight are the ones designing it, self-regulation is the outcome, not a placeholder for one.
The evidence is on the calendar, not in a mood. SAFA announced September 25. A 417-3 House vote to protect ratepayers, dead in the Senate two days before that. A rare-earth suspension that lapses November 10. A near-certain El Nino the WMO now calls very strong, peaking near a record at year's end. NERC adding 224 gigawatts to its ten-year forecast. Half a dozen separate clocks, all bunched into the same eight weeks.
I'd watch October into December hardest. That's when the rare-earth cliff either lapses or gets waved off at a Trump-Xi table, when El Nino peaks and starts breaking whatever it breaks, when Google's first chips either survive orbit or don't, and when SAFA either signs up real auditors or stays a press release with famous names attached. A lot of bets settle 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 still grow teeth — FINRA has them, sometimes — and a deadline is a negotiation, not a wall; the rare-earth freeze already got extended once. The likeliest outcome isn't a single cliff, it's slippage: everything a little slower, a little more gas-fired, a little more governed-by-the-governed than anyone wanted to admit. And the development I keep circling back to isn't on the countdown at all — this fall a gene edit inside a living body kept working, while everyone else was busy deciding who holds the whistle.
Cross-Domain Synthesis
This week the three labs that actually build frontier models — Google, OpenAI and Anthropic — announced they're setting up their own referee. It's called SAFA, modeled on FINRA, the body Wall Street uses to police itself. Read that twice: the companies building the technology are staffing the group that will decide whether it's safe. Last week 22 governments asked for rules and the big players skipped the table. This week the big players built their own table. That's not a coincidence, it's a strategy.
Meanwhile the physical layers kept ignoring the paperwork. China's rare-earth freeze still expires November 10, and the chip inputs everyone needs clear one license at a time. US data-center demand keeps climbing, and NERC now sees 224 more gigawatts of peak load over the decade — 69% more than it guessed a year ago — with more than half of that power still burning something. The reactors everyone's counting on don't scale until the 2030s, so gas is the bridge.
And the bill landed. The House voted 417 to 3 to stop data centers from dumping their power costs on everyone else — then a single senator killed it in the Senate, on the grounds that a bill which only asks states to 'consider' the problem has no teeth. He's not wrong. Which tells you where consent actually sits: everyone agrees the buildout is shifting costs onto households, and Washington still can't pass the fix.
The thing that answers to none of it is biology. A gene edit inside a living person is still holding LDL down a year later, a pig kidney ran for 271 days, and prime editing quietly got dozens of times better in a mouse liver. No cliff, no cloture vote, no summit. The loud clocks this fall all have deadlines. The one that might matter most is just compounding in the background while everyone argues over who gets to hold the whistle.
05
Horizon: Predictive convergence
Futurology Report — Daily
September 26, 2026 at 1:36:53 AM
AI Model:
Opus 4.8
Forecast:
2026–2028
The three companies that build the frontier stood up their own regulator (SAFA) instead of waiting for one — the same week the Senate killed the 417-3 ratepayer bill, the rare-earth cliff held for Nov 10, and a near-100% El Nino aimed past every modern record. Through 2028 the binding constraint is who writes the rules, not whether the models can.
Here's the bet through 2028: the fight was never about whether the models work — it's about who gets to write the rules, and this week that fight got more honest. The three companies building the frontier stopped waiting for a government referee and stood one up themselves. Call it what it is — the industry would rather grade its own homework than let anyone else hold the pen. When the people with the most to lose from oversight are the ones designing it, self-regulation is the outcome, not a placeholder for one.
The evidence is on the calendar, not in a mood. SAFA announced September 25. A 417-3 House vote to protect ratepayers, dead in the Senate two days before that. A rare-earth suspension that lapses November 10. A near-certain El Nino the WMO now calls very strong, peaking near a record at year's end. NERC adding 224 gigawatts to its ten-year forecast. Half a dozen separate clocks, all bunched into the same eight weeks.
I'd watch October into December hardest. That's when the rare-earth cliff either lapses or gets waved off at a Trump-Xi table, when El Nino peaks and starts breaking whatever it breaks, when Google's first chips either survive orbit or don't, and when SAFA either signs up real auditors or stays a press release with famous names attached. A lot of bets settle 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 still grow teeth — FINRA has them, sometimes — and a deadline is a negotiation, not a wall; the rare-earth freeze already got extended once. The likeliest outcome isn't a single cliff, it's slippage: everything a little slower, a little more gas-fired, a little more governed-by-the-governed than anyone wanted to admit. And the development I keep circling back to isn't on the countdown at all — this fall a gene edit inside a living body kept working, while everyone else was busy deciding who holds 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
93
%
BIOTECH
66
%
GEOPOLITICS
80
%
ENERGY
94
%
SOCIETY
90
%
SPACE
64
%
Cross-Domain Synthesis
This week the three labs that actually build frontier models — Google, OpenAI and Anthropic — announced they're setting up their own referee. It's called SAFA, modeled on FINRA, the body Wall Street uses to police itself. Read that twice: the companies building the technology are staffing the group that will decide whether it's safe. Last week 22 governments asked for rules and the big players skipped the table. This week the big players built their own table. That's not a coincidence, it's a strategy.
Meanwhile the physical layers kept ignoring the paperwork. China's rare-earth freeze still expires November 10, and the chip inputs everyone needs clear one license at a time. US data-center demand keeps climbing, and NERC now sees 224 more gigawatts of peak load over the decade — 69% more than it guessed a year ago — with more than half of that power still burning something. The reactors everyone's counting on don't scale until the 2030s, so gas is the bridge.
And the bill landed. The House voted 417 to 3 to stop data centers from dumping their power costs on everyone else — then a single senator killed it in the Senate, on the grounds that a bill which only asks states to 'consider' the problem has no teeth. He's not wrong. Which tells you where consent actually sits: everyone agrees the buildout is shifting costs onto households, and Washington still can't pass the fix.
The thing that answers to none of it is biology. A gene edit inside a living person is still holding LDL down a year later, a pig kidney ran for 271 days, and prime editing quietly got dozens of times better in a mouse liver. No cliff, no cloture vote, no summit. The loud clocks this fall all have deadlines. The one that might matter most is just compounding in the background while everyone argues over who gets to hold the whistle.
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