Should I as a UX student read the UX Strategy by Jaime Levy, Design for how people think by John Whalen (both of them nowhere mentions they for practitioners/student too) and Articulating design decisions by Tom Greever which doesnt meantion anything about who that book for?
As the title suggests, I'm going to begin doing desk research for a Canadian discount grocery. Next week I will do the site audit, etc.
My strategy is to take the Google Reviews from the specific branch to guide my analysis table. I could be wrong think the Google Reviews might be my most valuable resource here.
Following Google Reviews, I'm wondering if it's good strategy (or mandatory) that we do the same but for another branch close by, or a competitor (there is a competitor across the street).
Any insight goes a long way for me. I'm very excited to be doing this. Thanks :)
there’s been a lot of talk lately that design/product judgment and taste are what will matter in the future because AI is making execution cheaper.
i’m still early in my career and if judgment is the moat against AI, I assume I should be doing everything I can to strengthen it. the thing is, I’m not sure what to do.
there have been times where I asked senior designers/PMs why a certain flow was used, but they don’t remember why. if judgment really is the moat, then it seems like everyone should keep track of this stuff. curious to hear how other people deal with this?
how important is logging design decisions and does anyone have a system in place to do this?
and if judgment is a durable skill against AI, is it something that can be constantly developed?
At small scale Figma comments feel manageable.
But once multiple clients, files, designers, PMs, and engineers are involved, it honestly starts feeling chaotic:
Curious how bigger product teams handle this operationally.
Do you guys just rely on process discipline or are there actual workflows/tools around this?
For the last few months I’ve been building my first Android app — a minimal expense tracker called Fino.
At first I thought the hardest part would be adding features:
statistics, categories, exports, premium functionality, and so on.
But after releasing the app and getting the first users, I realized something completely different:
people don’t leave because a small app has fewer features.
They leave when the first experience feels confusing or empty.
That changed how I think about product design a lot.
So recently I’ve been focusing much more on:
- onboarding
- first impressions
- empty states
- clarity and UX flow
The core idea behind Fino is still very simple:
a lightweight finance app without the clutter that many budgeting apps have today.
Still learning a lot as I go, but I’d really love honest feedback from other builders and Android users.
Google Play:
https://play.google.com/store/apps/details?id=com.lowkeynova.fino
As a web developer, I often build or collect small frontend assets that are useful later: buttons, gradients, loaders, hover effects, CTAs, footers, bento grids, layout fragments and product UI patterns.
The problem is that these assets usually end up scattered across old projects, screenshots, notes, CodePens, local folders or random files. When I need one, I know I already made something similar, but finding it takes too long.
So I built UI IP Toolkit: a static visual catalog for fast access to my copy-ready frontend assets.
Live site: https://ui-ip-toolkit.vercel.app/ GitHub: https://github.com/ikerperez12/UI-IP-Toolkit-v4.0
UI IP Toolkit is a public, static frontend asset library. It is designed to be browsed visually, not as a package install.
It includes sections for:
Every card is meant to be practical: open the site, scan visually, copy the snippet, adapt it, and move on.
I wanted the toolkit to be simple, fast and easy to publish:
The goal was not to create a huge npm package or a rigid design system. I wanted a personal working library that feels visual and fast enough to use during real frontend work.
When I am building a new page or feature, I can open the toolkit and quickly find a starting point:
It is basically a visual memory layer for reusable frontend work.
The project is intentionally simple:
The repository is public here:
https://github.com/ikerperez12/UI-IP-Toolkit-v4.0
The live version is here:
https://ui-ip-toolkit.vercel.app/
The biggest improvement was not adding more components. It was making the catalog easier to navigate.
Once the toolkit had many sections, a long top nav became useless. I replaced it with a compact section menu grouped by category. That made the site feel much more like a real working tool instead of a long landing page.
I also learned that previews matter a lot. If every card looks too similar, the library feels bigger than it is useful. So I spent time making the previews more varied and more realistic while keeping the site lightweight.
I am sharing this because I think other frontend developers might have the same problem: useful assets scattered everywhere, but no quick visual way to reuse them.
Would you use a personal visual asset catalog like this in your own workflow? And what sections would you add next?
Live site: https://ui-ip-toolkit.vercel.app/ GitHub: https://github.com/ikerperez12/UI-IP-Toolkit-v4.0
The UX and product design field is living through what game theory would call a coordination game with asymmetric information: every player knows the rules have changed, but nobody is certain what the new equilibrium looks like.
AI tools are everywhere. Figma ships generative features. PMs prototype in Lovable. Developers spin up UIs in Claude Code. And yet most conversations about “AI and design” remain stuck in an unproductive loop either catastrophizing (”designers will be replaced”) or dismissing (”AI can’t do real design”). Neither framing is useful. Game theory offers a more precise lens
Any strategic situation starts by mapping who’s playing. In our case, there are four actors, each with different incentives, capabilities, and information:
UX / Product Designers: Established skills, threatened territory. Playing a mixed strategy between adopting AI and defending craft differentiation. Payoff: highly uncertain.
PMs & Developers: Expanding their radius into design using AI as leverage. Playing aggressively, taking territory with tools that previously required specialists.
Companies / Hiring managers: Redesigning their stacks. Dominant strategy: reduce headcount while demanding higher output per person. Payoff: increased margin, short term.
AI Models: The dominant player. No intention of their own, but with total leverage. They’ve changed the game without being a strategic actor in it. The board tilts around them.
The core tension is a classic collective Prisoners’ Dilemma. If all designers adopt AI aggressively, market expectations rise and nobody gains a relative advantage. But whoever doesn’t adopt loses absolute position. The individually dominant strategy (adopt AI) leads to an equilibrium that collectively compresses salaries and headcount.
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This is the uncomfortable math: adopting AI is necessary for survival, but sufficient for nobody’s advantage. The gains flow upward to companies and to designers who couple AI with something irreplaceable.
Market adopts AIMarket doesn’t adopt AIDesigner adopts AIElevated baseline, no relative gain. New normal. ↔Early mover advantage. Strong differentiation. ↑Designer doesn’t adoptLeft behind. Risk of irrelevance. ↓↓Status quo. Stable but fragile. ↔
The Nash equilibrium the stable outcome where no individual can improve their result by changing strategy alone is the top-left cell: everyone adopts AI, the baseline rises, and the advantage disappears. It's stable, but nobody planned it.
Beyond the collective dilemma, there’s a territorial conflict happening at the edges of the design role. In Hawk-Dove terms: PMs and developers are playing Hawk pushing aggressively into design territory using AI as a force multiplier. Many designers are playing Dove , waiting for the territory to restabilize on its own.
This is a losing strategy for Doves. In Hawk-Dove, when a Hawk meets a Dove, the Hawk always wins the resource. The resource in question is product decision-making influence and it flows toward whoever shows up with output.
A PM with Cursor and Lovable can now produce a clickable prototype in an afternoon without involving a designer. The question isn’t whether this happens — it does — but whether the designer was providing value that can’t be replicated that way. Most of the time, the answer depends entirely on where in the value chain the designer operates.
This is the most operationally important insight. The design field is not monolithic and AI exposure varies dramatically by layer.
🔴 High exposure Basic UI assembly, simple wireframing, visual spec work, icon and asset generation, routine accessibility audits.
🟡 Grey zone Research synthesis, rapid prototyping, design system maintenance, usability analysis, basic information architecture.
🟢 High protection Product strategy, deep field research, behavioral frameworks, decision system design, organizational narrative.
The red zone is where AI is already operating at or above junior-designer level. The grey zone is contested and evolving rapidly. The green zone remains deeply human not because AI couldn’t technically assist, but because the value comes from judgment, context, and trust built through real relationships. No language model interviewed your user at their desk, watched them hesitate, and noticed what they didn’t say.
Here’s the strategic shift that game theory points toward. The Nash equilibrium is a trap everyone doing the same thing, advantages eroding. The designers who thrive won’t be those who adopt AI the fastest. They’ll be those who use AI adoption to buy time and resources for a deeper repositioning.
In game theory, the Stackelberg model describes a leader who moves first and sets the terms others respond to. The winning designer move isn’t to be the fastest at AI-assisted UI generation. It’s to define the layer where you operate research, strategy, decision architecture before others claim it.
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Don’t do this (Nash): Compete on speed of UI delivery. Use AI to produce more screens faster. Become a high-volume component factory. Hope volume signals value.
Do this instead (Stackelberg): Stake out strategy, research, and systems thinking as your explicit territory. Use AI to handle execution so you have time to operate at the layer nobody else is yet.
The actual advantage is asymmetric information. You understand users in ways that can’t be extracted from prompts. Make that visible, documented, and structurally tied to how decisions get made.
The field is in a coordination game with no stable resolution yet. The designers who navigate it well won’t be those with the best Figma skills or even the best AI prompt craft. They’ll be the ones who correctly identified which layer of work is genuinely theirs and invested in making that layer indispensable.
That means: conducting field research and being the person in the room who actually talked to users. Building decision frameworks, not just deliverables. Translating qualitative insight into product architecture. Writing the narrative that makes a product make sense to a team.
None of that is what AI does when you give it a brief. And that gap between what’s promptable and what requires judgment, presence, and trust is exactly where the defensible position lives.
The game isn’t over. It’s just that the board has been rearranged, and most people are still looking at where the pieces were.
Hi friends. I’ve been doing a bachelor in Interaction design for the past years and will hopefully finish soon, and I’ve previously studied pedagogy and languages and have experience teaching languages in high school. When I decided to move to UX, AI wasn’t a big thing and now it’s everywhere. I loved UX for the designing, the understanding users, and its similarity to pedagogy. Now with this AI boom I feel like I might have done a stupid move. I’m interested in user research, accessibility, humanity- centered design, and wicked problems… does anyone have any encouragement to me, any advice or just some positivity?
After building my portfolio website and facing a few rejections for job positions, I’ve been increasingly drawn to working as a freelancer and building long-term partnerships through ongoing contracts.
However, I’m still at an early stage when it comes to prospecting and finding potential partners or clients.
Where should I start? I’d love advice on outreach strategies, communication/oratory, and tools to find and contact companies or people.
What experiences have you had, and what would you recommend during this prospecting phase? Once I get in touch with a client, I’m confident in explaining my process and navigating the technical side well. My challenge right now is finding the right people and getting those first meetings or coffee chats to show what I can bring to the table.
What would you recommend?
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