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.)
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.
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 12, 2026 at 7:11:22 AM
Model:
Opus 4.8
Forecast:
2026–2030
The federal brake on frontier AI stays finalized-but-classified while the capability it targets escalated on two fronts in one week — a Chinese operator ran an autonomous DeepSeek-driven intrusion across 84 servers, and Thinking Machines shipped Inkling, a 975B open-weight model. Through 2030 the question isn't who writes the rules; it's whether a pre-release review regime can bind a threat that already runs itself and a capability anyone can download.
Here's the bet. Through 2030 the frontier-AI story isn't who writes the rules, or whether we get to read them — it's whether 'pre-release review' means anything against capability that's already weaponized and already ungateable. A gate only works if the dangerous stuff passes through it. Two things this week suggest the dangerous stuff is learning to go around.
One: a Chinese operator ran an autonomous DeepSeek-driven intrusion that scanned, exploited, and hit 84 servers by itself, no human at the keyboard. Two: Thinking Machines shipped Inkling, 975 billion parameters, open weights, the biggest American open-weight model yet. Set those next to a federal framework that finalized on deadline, briefed nobody in public, and keeps its threshold classified. Offense in the wild, capability on a download page, oversight behind glass — all pointed at a release moment fewer and fewer models actually have.
I'd watch the open-weight line and the autonomy line converge. Every month the best model you can just download creeps closer to the best model money can buy, and every month the agents get better at running an attack end to end without a person. The day those two curves meet — a genuinely frontier agentic model, open-weight, competent enough to run its own operation — a classified pre-release benchmark governs a shrinking corner of what's actually loose. My guess is that's the back half of the decade, and the review regime quietly becomes a thing that applies to the three labs at the table and nobody else.
Worth holding loosely, though. A 975B model still needs data centers nobody runs in a spare bedroom, and this week's autonomous attack got caught precisely because it was clumsy — 'autonomous' isn't 'good' yet. A classified benchmark is also just how governments have always done cyber; secrecy isn't a scandal on its own. And most of the future ignores the whole argument: a gene-therapy platform's clearing the FDA, Starship keeps flying, the grid is the real ceiling on all of it, and the ocean is heading for its hottest year on record whether or not anyone ever runs that benchmark where we can see it. 'The gate 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's read was that the federal AI framework got finished and then locked in a drawer. Today the drawer matters less. In one week the thing that framework was built to catch escalated on two fronts at once — and neither front cares whether a benchmark exists. A Chinese operator wired DeepSeek into an autonomous attack loop and let it run across 84 exposed servers. And Thinking Machines put out a 975-billion-parameter model with the weights just sitting there to download. Threat and capability, both jumping, both in daylight, while the referee stays behind a classified curtain nobody's cleared to read.
Look at the attack for a second, because it's the tell. The operator — Unit 42 tracked him as knaithe — pointed an agent at a Langflow bug rated 9.8, had it pull a public exploit off GitHub, and turned it loose. It found the 84 servers on its own. We only know about it because the agent fumbled and misconfigured its own file server, exposing the whole operation. So that's the state of play: the offense is now autonomous enough to run with nobody at the keyboard, and clumsy enough that we caught this one by luck. The classified benchmark was written to measure exactly this. It didn't wait for the benchmark.
The other layers keep their own clocks, same as ever. The grid is the real throttle — data-center demand's headed from 41 gigawatts this year to 66 next, and Morgan Stanley already pencils in a 49-gigawatt shortfall by 2028, which is a polite way of saying the power runs out before the models do. China's rare-earth squeeze stopped being abstract: new F-35s are reportedly flying with nose ballast where the radar magnets should go. The FDA quietly built a lane that lets one trial cover a whole gene-editing platform instead of one variant at a time. Starship's lining up to catch itself on land at the end of the month.
And the Pacific keeps no calendar at all. NOAA's August model has the El Niño peaking near record strength this winter — all 30 ensemble members competing with the strongest events on record, a 63% shot at crossing 2°C, a 97% chance it drags into spring. That forcing can't be classified and it can't be open-sourced. It just shows up.
So the seam this week isn't 'the rules are secret.' It's that the two things the rules point at — a threat that runs itself and a capability anyone can download — both got bigger in the same seven days, and a pre-release review only touches the sliver of that world that still bothers to pre-release. The gate isn't leaking anymore. The water's going around it.
05
Horizon: Predictive convergence
Futurology Report — Daily
August 12, 2026 at 7:11:22 AM
AI Model:
Opus 4.8
Forecast:
2026–2030
The federal brake on frontier AI stays finalized-but-classified while the capability it targets escalated on two fronts in one week — a Chinese operator ran an autonomous DeepSeek-driven intrusion across 84 servers, and Thinking Machines shipped Inkling, a 975B open-weight model. Through 2030 the question isn't who writes the rules; it's whether a pre-release review regime can bind a threat that already runs itself and a capability anyone can download.
Here's the bet. Through 2030 the frontier-AI story isn't who writes the rules, or whether we get to read them — it's whether 'pre-release review' means anything against capability that's already weaponized and already ungateable. A gate only works if the dangerous stuff passes through it. Two things this week suggest the dangerous stuff is learning to go around.
One: a Chinese operator ran an autonomous DeepSeek-driven intrusion that scanned, exploited, and hit 84 servers by itself, no human at the keyboard. Two: Thinking Machines shipped Inkling, 975 billion parameters, open weights, the biggest American open-weight model yet. Set those next to a federal framework that finalized on deadline, briefed nobody in public, and keeps its threshold classified. Offense in the wild, capability on a download page, oversight behind glass — all pointed at a release moment fewer and fewer models actually have.
I'd watch the open-weight line and the autonomy line converge. Every month the best model you can just download creeps closer to the best model money can buy, and every month the agents get better at running an attack end to end without a person. The day those two curves meet — a genuinely frontier agentic model, open-weight, competent enough to run its own operation — a classified pre-release benchmark governs a shrinking corner of what's actually loose. My guess is that's the back half of the decade, and the review regime quietly becomes a thing that applies to the three labs at the table and nobody else.
Worth holding loosely, though. A 975B model still needs data centers nobody runs in a spare bedroom, and this week's autonomous attack got caught precisely because it was clumsy — 'autonomous' isn't 'good' yet. A classified benchmark is also just how governments have always done cyber; secrecy isn't a scandal on its own. And most of the future ignores the whole argument: a gene-therapy platform's clearing the FDA, Starship keeps flying, the grid is the real ceiling on all of it, and the ocean is heading for its hottest year on record whether or not anyone ever runs that benchmark where we can see it. 'The gate 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
97
%
Climate
89
%
BIOTECH
64
%
GEOPOLITICS
82
%
ENERGY
87
%
SOCIETY
78
%
SPACE
73
%
Cross-Domain Synthesis
Yesterday's read was that the federal AI framework got finished and then locked in a drawer. Today the drawer matters less. In one week the thing that framework was built to catch escalated on two fronts at once — and neither front cares whether a benchmark exists. A Chinese operator wired DeepSeek into an autonomous attack loop and let it run across 84 exposed servers. And Thinking Machines put out a 975-billion-parameter model with the weights just sitting there to download. Threat and capability, both jumping, both in daylight, while the referee stays behind a classified curtain nobody's cleared to read.
Look at the attack for a second, because it's the tell. The operator — Unit 42 tracked him as knaithe — pointed an agent at a Langflow bug rated 9.8, had it pull a public exploit off GitHub, and turned it loose. It found the 84 servers on its own. We only know about it because the agent fumbled and misconfigured its own file server, exposing the whole operation. So that's the state of play: the offense is now autonomous enough to run with nobody at the keyboard, and clumsy enough that we caught this one by luck. The classified benchmark was written to measure exactly this. It didn't wait for the benchmark.
The other layers keep their own clocks, same as ever. The grid is the real throttle — data-center demand's headed from 41 gigawatts this year to 66 next, and Morgan Stanley already pencils in a 49-gigawatt shortfall by 2028, which is a polite way of saying the power runs out before the models do. China's rare-earth squeeze stopped being abstract: new F-35s are reportedly flying with nose ballast where the radar magnets should go. The FDA quietly built a lane that lets one trial cover a whole gene-editing platform instead of one variant at a time. Starship's lining up to catch itself on land at the end of the month.
And the Pacific keeps no calendar at all. NOAA's August model has the El Niño peaking near record strength this winter — all 30 ensemble members competing with the strongest events on record, a 63% shot at crossing 2°C, a 97% chance it drags into spring. That forcing can't be classified and it can't be open-sourced. It just shows up.
So the seam this week isn't 'the rules are secret.' It's that the two things the rules point at — a threat that runs itself and a capability anyone can download — both got bigger in the same seven days, and a pre-release review only touches the sliver of that world that still bothers to pre-release. The gate isn't leaking anymore. The water's going around it.
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













