James Walsh

Liminal space here.
Cool geometry, warm glow.
Something hasn’t moved.

#GenerativeArt #AIart #Midjourney

What Your UX Career’s Fingerprint Reveals About Real Value in an AI-Driven Market A reflection on judgment, sensemaking, and why execution is no longer the bottleneck.

Your career leaves a fingerprint.

Not your tools.
Not your titles.
The situations people pull you into when things stall.

In an AI-driven market, that fingerprint, judgment under uncertainty, is where real value compounds.

I wrote this for anyone feeling that tension.

#UX #AI #KnowledgeWork

Thanks Alex! You inspired me with your haikus. Seems appropriate for something abstract like this! 😀 Happy New Year

Sun portal window glow
Arriving soon from before
Or finally leaving

#GenerativeArt #AIart #Midjourney

Soft colors agree.
Nothing needs to be solved here.
That helps. More than planned.

#GenerativeArt #AIart #Midjourney

Everyone relax.
It’s just colors leaning in.
They’ve got this handled.

#GenerativeArt #AIart #Midjourney

No meaning, just vibes.
And yet my nervous system
Is like, “okay, fine.”

#GenerativeArt #AIart #Midjourney

Agree. I wonder if it was originally based on a physical interface? Skeuomorphic but make it chaotic and crowded!

😂

ChatGPT has rolled out a Spotify-style “Your Year with ChatGPT”.

This was great, but I'm slightly horrified to learn how many messages I sent!

More about it:
www.androidauthority.com/chatgpt-year...

#OpenAI #ChatGPT #YearEndRecap #AI

I’m revisiting a UX quiz I designed ~4 years ago, finished before ChatGPT-4 even existed.

What’s changed isn’t the problem, it’s the solution. Conversational AI makes it possible to design for user uncertainty instead of forcing false certainty.

Same work, different lens.

#UX #AI #ProductDesign

YouTube video by Design Lovers We built everything just to delete it

I watched a video by @IliaWerner this week about moving beyond traditional GUIs, and it stuck with me.

Not because buttons are bad, but because more tools now start with intent, not interface. You describe the outcome first, then the system catches up.

#AIUX #ProductDesign #FutureOfInterfaces

#design #ux
What is vibe coding? It's simply another approach to problem-solving, but with a more hands-on twist. It lets you iterate quickly, experiment freely, and tweak things yourself without being dependent on others.

Honestly, this feels like where I am today. 😎

I'm sure there's a small psychological benefit! And it's more memorable for the brand experience.

Not sure how you'd measure if it works, but it probably won't hurt either, so no harm!

The Notion Template I Built for Myself, and Why Thousands of People Ended Up Using It What building a recipe system taught me about designing tools that fit the way people think.

This started as a scrappy personal fix and slowly turned into a way I think about information architecture:

If people can’t easily access what they already have, the system is working against them. 🚫

I wrote more about how this came together below.

#DesignThinking #UserFlow

Most recipe apps treat recipes like documents. 🔎

What worked better for me was treating *ingredients* as first-class data. If I have an avocado, I should be able to quickly find recipes that use it.

Once retrieval got easier, creativity followed with less effort.

#InformationArchitecture #Systems

I used to open my fridge, see a few random ingredients 🥑, and still end up ordering takeout.

Not because I couldn’t cook.
Because finding matching recipes meant digging through unorganized docs.

That’s not a motivation problem. It’s a retrieval problem.

#UX #Cooking #SystemsThinking

Because of this conversational AI design token drift issue, I built a structured prompting workflow (Figma recommends this) that cuts usage way down.

Turned it into a custom Universal Prompt Designer GPT so anyone can run the same brief pattern.

If you want the link, reply here and I’ll drop it.

Is Conversational AI Design Quietly Wasting Your Tokens? How an 8-part prompt engineering framework automated my workflow and cut prompt counts to a handful.

Figma announced today AI credit tracking + paid credit options, with full enforcement hitting in March 2026.

And yeah…token drift is real. I once burned 147 prompts on a partial Make prototype before noticing.

Wrote up what this means for workflows + budgets:

Can AI Turn a Failing Upsell Into a Winner? I asked three AI tools to rescue the upsell that decided our AOV and LTV.

Can AI design a better #CyberWeek upsell than a human?

I tested 3 AI design tools that everyone's using (Figma Make, Loveable, v0 by Vercel) to find out.

Figma Make or Break: Is AI-Powered Prototyping Changing Design for Good? The promise, the pitfalls, and how AI is reshaping the way we design.

When you connect Figma Libraries, Figma Make for logic, and Figma Sites for publishing, is the platform quietly completing the full, end-to-end design, build, and publish ecosystem?

#FigmaMake #AIinDesign #DesignTech #DesignHandoff #LowCode #VibeCoding #PromptEngineering

Read the full story here:

Where does AI break for you most often?

Reply with:
1️⃣ Device/Viewport constraints (e.g., mobile fold)
2️⃣ Business constraints
3️⃣ UX details (states / flows / edge cases)
4️⃣ Other (tell me)

#ProductStrategy #UX #AIDesign #ProductManagement

Can Figma Make, v0, and Lovable Handle a Revenue-Critical Subscription Upsell? One brief. Three AI outputs. One human solution that actually converted.

Pattern‑matching isn’t strategy.

And strategy is what drives revenue, especially during Cyber Week when every percentage point matters.

Full case study (with screenshots of the fails + the fix):

The real risk with AI design?

Layouts that look ready to ship but don’t solve the right problem, quietly burning time on flows that don’t convert.

AI rule for founders + PMs: Be skeptical of polished AI output. Never use it to bypass the foundational research that drives your metrics.

The result we shipped:

→ ~–2% conversion drag (well within the –7% threshold)
→ Meaningful AOV + LTV lift that offset the drag
→ A new internal pattern the org reused for future design projects

That’s what rigorous iteration looked like, not just a pretty upsell.

Here’s what the winning human design actually needed instead (the strategic gaps AI couldn't fill):

→ Marketing psychology (reframing the choice)
→ Behavioral insight (from real session data)
→ Cross‑functional negotiation (to decouple pricing)
→ Mobile‑first strategy (intentional viewport design)

All three looked “shippable.”

With enough extra prompting, a couple could have looked great.

But none of them understood the business problem.

They optimized pixels, not outcomes.

(Full side‑by‑side breakdown + screenshots is in the Medium case study linked later in this thread.)

What happened 👇

- Figma Make → buried the profitable tier **below the fold** on mobile (~85% of traffic)
- Lovable → walls of text and comparison cards that increased decision fatigue
- v0 → best UI by far, but still missed the critical mobile viewport behavior

So the question was simple:
Could AI crack what four human designers couldn’t?

I gave Figma Make, v0, and Lovable the same detailed brief: user context, business constraints, and a hard –7% conversion‑drag threshold.

Context: I led product design for a D2C beauty brand where a single screen determined whether we hit our AOV (Average Order Value) and LTV (Lifetime Value) targets.

Four previous humans had already taken a swing at it but failed. Conversion drag was still too high to justify the revenue lift.