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 29, 2026 at 12:57:29 AM
Model:
Opus 4.8
Forecast:
2026-2028
Three labs built their own AI referee and can't name a thing it can force; California passed a real chatbot law, the Senate stalled a national one, the rare-earth cliff holds for Nov 10, PJM missed supply for the first time ever, the WMO called this El Nino its most certain forecast on record, and Google's first orbital chips go up Oct 1. Through 2028 the binding question isn't whether the models work — it's whether the power, the minerals and the rules switch on fast enough to matter, and who ends up holding each switch.
Here's the bet through 2028: the fight was never whether the models work. It's whether the power, the minerals and the rules switch on fast enough to matter, and who ends up holding each switch. This week made that harder to wave away. The labs stood up their own referee and couldn't name one thing it can force; the people it would govern asked Congress for a real one; California went ahead and passed its own. When you build a self-regulator and lobby for a federal one in the same breath, that's a hedge, not a plan.
The evidence is on the calendar, not in the vibe. SAFA, weeks old and already picked apart. A 417-3 House vote, stalled in the Senate days later. A rare-earth suspension that lapses November 10. PJM missing supply for the first time and scrambling $20 billion for plants by September 30. A WMO El Nino call it says is the most certain it's ever issued, cresting at year's end. 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 PJM's auction either lands the megawatts or doesn't, when Google's first chips either survive orbit or don't, and when the midterms turn power bills into votes. A lot settles in one quarter — and given the year, I wouldn't bet the whole board on the calm read.
Worth holding loosely, though. A toothless self-regulator can grow teeth — FINRA did, eventually — 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 thing I keep circling back to isn't on the countdown at all — this fall an edit inside a living body just kept working, for months, while the rest of us argued about the whistle.
Cross-Domain Synthesis
The seam this week is the same one, pulled a little tighter. Three labs built themselves a referee — SAFA, a FINRA-for-AI they want live by early 2027 — and the more people read the fine print, the less it looks like it can do. No power to block a launch. No power to fine anyone. So the odd thing keeps happening: the companies that stood up the private referee are also asking Washington for a real one. And this week California didn't wait — Newsom signed a law making chatbot makers run risk checks and pay up to $1M per child they harm. The question was never whether anyone regulates this. It's who, and whether they can make it stick.
The thing being regulated keeps outrunning the paperwork. Four labs shipped frontier models in the first week of September, and GPT-6 Astra is posting near-perfect scores on the hard benchmarks. You can't finish a standard for a model that's two versions old by the time you publish it. Meanwhile the physical layers keep dates the software doesn't. China's rare-earth freeze lapses November 10, and the Trump-Xi meeting in Washington on the 24th put the deadline on the table without moving it — analysts still call it relief without resolution. PJM, the biggest grid in the country, couldn't buy enough power for the first time ever, and its fix is a $20 billion scramble for plants, with the auction landing around September 30. The megawatts get built or they don't.
That bill went straight to the kitchen table. The House voted 417 to 3 to make data centers pay for their own grid upgrades, and days later the Senate couldn't fast-track it — one camp wants FERC to actually have jurisdiction, the other wants voluntary state deals. Capacity prices went from $29 a megawatt-day to $329 in two years. Bills are up double digits in Virginia, Illinois and Ohio. Amazon, Microsoft and a line of fusion startups keep signing reactors, but none of that switches on in time to help, so near-term the gap burns gas. Overhead, Google's about to find out whether AI chips survive orbit — Suncatcher goes up October 1. Every one of these has a date, and they're all crammed into the same eight weeks.
The layer that answers to none of it is biology. A gene-edited pig kidney ran 271 days without dialysis and left the patient clean for a human transplant afterward. A delivery tweak just pushed in-body editing to 67% of a mouse liver in a single shot. And the ocean's on its own clock too — the WMO says this El Nino is the most certain forecast it's ever made, very strong, peaking at year's end, while the Arctic just closed out one of its ten lowest-ice years on record. No cliff, no cloture vote. While everyone fights over who holds the whistle, the slow layers keep compounding.
05
Horizon: Predictive convergence
Futurology Report — Daily
September 29, 2026 at 12:57:29 AM
AI Model:
Opus 4.8
Forecast:
2026-2028
Three labs built their own AI referee and can't name a thing it can force; California passed a real chatbot law, the Senate stalled a national one, the rare-earth cliff holds for Nov 10, PJM missed supply for the first time ever, the WMO called this El Nino its most certain forecast on record, and Google's first orbital chips go up Oct 1. Through 2028 the binding question isn't whether the models work — it's whether the power, the minerals and the rules switch on fast enough to matter, and who ends up holding each switch.
Here's the bet through 2028: the fight was never whether the models work. It's whether the power, the minerals and the rules switch on fast enough to matter, and who ends up holding each switch. This week made that harder to wave away. The labs stood up their own referee and couldn't name one thing it can force; the people it would govern asked Congress for a real one; California went ahead and passed its own. When you build a self-regulator and lobby for a federal one in the same breath, that's a hedge, not a plan.
The evidence is on the calendar, not in the vibe. SAFA, weeks old and already picked apart. A 417-3 House vote, stalled in the Senate days later. A rare-earth suspension that lapses November 10. PJM missing supply for the first time and scrambling $20 billion for plants by September 30. A WMO El Nino call it says is the most certain it's ever issued, cresting at year's end. 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 PJM's auction either lands the megawatts or doesn't, when Google's first chips either survive orbit or don't, and when the midterms turn power bills into votes. A lot settles in one quarter — and given the year, I wouldn't bet the whole board on the calm read.
Worth holding loosely, though. A toothless self-regulator can grow teeth — FINRA did, eventually — 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 thing I keep circling back to isn't on the countdown at all — this fall an edit inside a living body just kept working, for months, 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
96
%
Climate
94
%
BIOTECH
63
%
GEOPOLITICS
81
%
ENERGY
97
%
SOCIETY
90
%
SPACE
66
%
Cross-Domain Synthesis
The seam this week is the same one, pulled a little tighter. Three labs built themselves a referee — SAFA, a FINRA-for-AI they want live by early 2027 — and the more people read the fine print, the less it looks like it can do. No power to block a launch. No power to fine anyone. So the odd thing keeps happening: the companies that stood up the private referee are also asking Washington for a real one. And this week California didn't wait — Newsom signed a law making chatbot makers run risk checks and pay up to $1M per child they harm. The question was never whether anyone regulates this. It's who, and whether they can make it stick.
The thing being regulated keeps outrunning the paperwork. Four labs shipped frontier models in the first week of September, and GPT-6 Astra is posting near-perfect scores on the hard benchmarks. You can't finish a standard for a model that's two versions old by the time you publish it. Meanwhile the physical layers keep dates the software doesn't. China's rare-earth freeze lapses November 10, and the Trump-Xi meeting in Washington on the 24th put the deadline on the table without moving it — analysts still call it relief without resolution. PJM, the biggest grid in the country, couldn't buy enough power for the first time ever, and its fix is a $20 billion scramble for plants, with the auction landing around September 30. The megawatts get built or they don't.
That bill went straight to the kitchen table. The House voted 417 to 3 to make data centers pay for their own grid upgrades, and days later the Senate couldn't fast-track it — one camp wants FERC to actually have jurisdiction, the other wants voluntary state deals. Capacity prices went from $29 a megawatt-day to $329 in two years. Bills are up double digits in Virginia, Illinois and Ohio. Amazon, Microsoft and a line of fusion startups keep signing reactors, but none of that switches on in time to help, so near-term the gap burns gas. Overhead, Google's about to find out whether AI chips survive orbit — Suncatcher goes up October 1. Every one of these has a date, and they're all crammed into the same eight weeks.
The layer that answers to none of it is biology. A gene-edited pig kidney ran 271 days without dialysis and left the patient clean for a human transplant afterward. A delivery tweak just pushed in-body editing to 67% of a mouse liver in a single shot. And the ocean's on its own clock too — the WMO says this El Nino is the most certain forecast it's ever made, very strong, peaking at year's end, while the Arctic just closed out one of its ten lowest-ice years on record. No cliff, no cloture vote. While everyone fights over who holds the whistle, the slow layers keep 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