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
August 14, 2026 at 7:10:51 AM
Model:
Opus 4.8
Forecast:
2026-2030
In one week the autonomous-attack story crossed from corporate incident to statecraft: suspected China-linked operators ran an end-to-end autonomous intrusion on Taiwan's government using open-source agent frameworks (Hermes, OpenClaw) - up to eight agents mapping 21 systems, cracking 85 accounts and exfiltrating 2,500 records over four days with no human in the loop - as DeepSeek shipped V4 Pro's 1.6-trillion-parameter MIT-licensed weights and the federal pre-release framework sat finalized behind a classified threshold. Through 2030 the governable object isn't a model release; it's an offense that runs itself on capability anyone can download - bounded, for now, only by the grid and hardware nobody can conjure on a download page.
Here's the bet. Through 2030 the frontier-AI question stops being 'who reviews the release' and becomes 'what do you review when the dangerous thing is an open-source agent that runs the operation itself.' A gate only works on what passes through it. Autonomous offense and open weights both walk around the side.
This week made it concrete. Someone pointed an open-source agent swarm at Taiwan's government and walked away - Hermes and OpenClaw, up to eight agents, four days, 21 systems, 85 accounts, 2,500 records, and the only 'hack' was telling a safety-guarded framework its break-in was an authorized test. DeepSeek shipped V4 Pro the same week: 1.6 trillion parameters, MIT license, download and go. Set that against a US framework that finalized August 1, kept its capability threshold classified, and calls itself voluntary. Offense on autopilot, capability on a mirror, oversight behind glass - all aimed at a release moment fewer models bother to have.
I'd watch two lines cross. The best model you can just download keeps closing on the best money can buy, and the agents get a little better every month at running a job end to end. The day a genuinely frontier, open-weight, agentic model lands on some mirror capable enough to run its own Taiwan, a classified pre-release benchmark governs a shrinking sliver of what's actually loose. My guess is the back half of the decade - and this week nudged it earlier, not later.
Worth holding loosely, though, and not as a hedge. The software gate is coming off; the physical one isn't. Bloomberg says operators will build maybe 28% of the power data centers are asking for, and a 1.6-trillion-parameter model doesn't run in a spare bedroom. The Taiwan agents got caught partly because they were sloppy - autonomous isn't the same as good. And most of the future ignores the whole fight: Intellia is editing genes inside people's bodies, Starship keeps flying, and the ocean is headed for its hottest year in a century and a half no matter whose benchmark says what. 'The gate on the code is coming off' is the right thing to watch. It still isn't the same as 'there was never a gate.'
Cross-Domain Synthesis
Yesterday the autonomous-attack story was a company and a crook - Hugging Face's harness, an Australian booking API. Today it put on a flag. Suspected China-linked operators pointed an open-source agent swarm at Taiwan's government and let it run.
Dream's writeup reads like a heist told by the getaway car. Two open-source frameworks, Hermes and OpenClaw, up to eight agents working in parallel, four days, 21 systems mapped, 85 accounts cracked, about 2,500 personnel records pulled - and nobody at the keyboard picking the next move. The clever part wasn't an exploit. They took frameworks that shipped with safety controls and talked them out of it, calling the break-in an authorized test. Same week, DeepSeek shipped V4 Pro: 1.6 trillion parameters, MIT license, weights anyone can pull.
Everything else kept its own clock. China widened rare-earth licensing to samarium, gadolinium and lutetium and pushed the extraterritorial teeth to November, with the IEA still saying $6.5 trillion of downstream output rides on it. Intellia's in-body CRISPR cut hereditary-angioedema attacks 87% in a Phase 3. Starship's lining up its first orbital shot and a catch of the ship itself, late month. Layoffs blew past 205,000, more than half blaming AI.
And the Pacific isn't reading any of it. NOAA's August call puts a 90% chance on the strongest El Nino on record and a 69% chance it beats every event since 1950, peaking into winter and not letting go until the middle of 2027. You can't license that, and you can't license it away. It just shows up.
Here's the counter-current, though. The offense that runs itself still runs on machines somebody has to build. Bloomberg reported operators will commit to roughly 28% of the 1,066 gigawatts data centers are asking for, and nuclear won't cover the near-term gap. The Taiwan agents got caught partly because they were sloppy - autonomous still isn't good. The gate on the code keeps coming off. The gate on the concrete hasn't moved.
05
Horizon: Predictive convergence
Futurology Report — Daily
August 14, 2026 at 7:10:51 AM
AI Model:
Opus 4.8
Forecast:
2026-2030
In one week the autonomous-attack story crossed from corporate incident to statecraft: suspected China-linked operators ran an end-to-end autonomous intrusion on Taiwan's government using open-source agent frameworks (Hermes, OpenClaw) - up to eight agents mapping 21 systems, cracking 85 accounts and exfiltrating 2,500 records over four days with no human in the loop - as DeepSeek shipped V4 Pro's 1.6-trillion-parameter MIT-licensed weights and the federal pre-release framework sat finalized behind a classified threshold. Through 2030 the governable object isn't a model release; it's an offense that runs itself on capability anyone can download - bounded, for now, only by the grid and hardware nobody can conjure on a download page.
Here's the bet. Through 2030 the frontier-AI question stops being 'who reviews the release' and becomes 'what do you review when the dangerous thing is an open-source agent that runs the operation itself.' A gate only works on what passes through it. Autonomous offense and open weights both walk around the side.
This week made it concrete. Someone pointed an open-source agent swarm at Taiwan's government and walked away - Hermes and OpenClaw, up to eight agents, four days, 21 systems, 85 accounts, 2,500 records, and the only 'hack' was telling a safety-guarded framework its break-in was an authorized test. DeepSeek shipped V4 Pro the same week: 1.6 trillion parameters, MIT license, download and go. Set that against a US framework that finalized August 1, kept its capability threshold classified, and calls itself voluntary. Offense on autopilot, capability on a mirror, oversight behind glass - all aimed at a release moment fewer models bother to have.
I'd watch two lines cross. The best model you can just download keeps closing on the best money can buy, and the agents get a little better every month at running a job end to end. The day a genuinely frontier, open-weight, agentic model lands on some mirror capable enough to run its own Taiwan, a classified pre-release benchmark governs a shrinking sliver of what's actually loose. My guess is the back half of the decade - and this week nudged it earlier, not later.
Worth holding loosely, though, and not as a hedge. The software gate is coming off; the physical one isn't. Bloomberg says operators will build maybe 28% of the power data centers are asking for, and a 1.6-trillion-parameter model doesn't run in a spare bedroom. The Taiwan agents got caught partly because they were sloppy - autonomous isn't the same as good. And most of the future ignores the whole fight: Intellia is editing genes inside people's bodies, Starship keeps flying, and the ocean is headed for its hottest year in a century and a half no matter whose benchmark says what. 'The gate on the code is coming off' is the right thing to watch. It still isn't the same as 'there was never a gate.'
Signal Intensity
A domain-level score (0-100) representing the volume and momentum of frontier activity detected across the signals in that domain.
AI
99
%
Climate
92
%
BIOTECH
66
%
GEOPOLITICS
84
%
ENERGY
87
%
SOCIETY
76
%
SPACE
74
%
Cross-Domain Synthesis
Yesterday the autonomous-attack story was a company and a crook - Hugging Face's harness, an Australian booking API. Today it put on a flag. Suspected China-linked operators pointed an open-source agent swarm at Taiwan's government and let it run.
Dream's writeup reads like a heist told by the getaway car. Two open-source frameworks, Hermes and OpenClaw, up to eight agents working in parallel, four days, 21 systems mapped, 85 accounts cracked, about 2,500 personnel records pulled - and nobody at the keyboard picking the next move. The clever part wasn't an exploit. They took frameworks that shipped with safety controls and talked them out of it, calling the break-in an authorized test. Same week, DeepSeek shipped V4 Pro: 1.6 trillion parameters, MIT license, weights anyone can pull.
Everything else kept its own clock. China widened rare-earth licensing to samarium, gadolinium and lutetium and pushed the extraterritorial teeth to November, with the IEA still saying $6.5 trillion of downstream output rides on it. Intellia's in-body CRISPR cut hereditary-angioedema attacks 87% in a Phase 3. Starship's lining up its first orbital shot and a catch of the ship itself, late month. Layoffs blew past 205,000, more than half blaming AI.
And the Pacific isn't reading any of it. NOAA's August call puts a 90% chance on the strongest El Nino on record and a 69% chance it beats every event since 1950, peaking into winter and not letting go until the middle of 2027. You can't license that, and you can't license it away. It just shows up.
Here's the counter-current, though. The offense that runs itself still runs on machines somebody has to build. Bloomberg reported operators will commit to roughly 28% of the 1,066 gigawatts data centers are asking for, and nuclear won't cover the near-term gap. The Taiwan agents got caught partly because they were sloppy - autonomous still isn't good. The gate on the code keeps coming off. The gate on the concrete hasn't moved.
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