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Original Source: https://abduzeedo.com/kindred-motorworks-steering-vintage-icons-toward-modern-future-mare-island-california

Kindred Motorworks: Steering Vintage Icons Toward a Modern Future on Mare Island in California

ibby
10/26 — 2025

On Mare Island, California, Kindred Motorworks transforms vintage icons into modern masterpieces—blending classic style with cutting-edge design and tech.

Walk through the doors of Kindred Motorworks’ 100-square-foot headquarters on Mare Island in Vallejo, California and you’re immediately struck by the hum of creation. The space, once a naval shipyard, is now a cathedral of modern craftsmanship. On one side, gleaming vintage Ford Broncos in various stages of rebirth. On another, Volkswagen Buses stripped, restored, and reimagined for today.

This is no ordinary assembly line. Behind every vehicle are more than 10,000 hours of prototyping, testing, and designing to ensure the reliability and performance of a modern car. Every detail, from Italian leather upholstery and stitching to the drivetrain, has been reconsidered. Customers can choose between gas-powered builds or fully electric versions, bridging nostalgia with future-forward sustainability.

The Founder’s Vision

Kindred was founded in 2019 by Rob Howard, whose automotive love started early with Matchbox cars, Road & Track magazines, and Porsche 911 posters taped to his bedroom wall. But the real spark came in Philadelphia, working alongside his dad and brothers to fix whatever broke in their house.

Howard recalls:

“Whenever something broke, it was me, my brothers, and my dad who grabbed the wrenches, got our hands dirty, and figured out how to make it work again. That instinct—to fix, to learn, to bring something back to life, became a lifelong passion.”

That passion shaped Kindred’s ethos. As Howard puts it:

“What if restoration were streamlined, transparent, and modernized, so enthusiasts like me could spend less time chasing parts and more time enjoying the drive?”

Kindred was born from that idea, to make the dream of owning a vintage vehicle possible without the constant headaches of repair.

The Team Behind the Builds

Kindred today is more than a founder’s vision. It’s a team of over 100 employees including technicians, software developers, and mechanical engineers, all working in harmony to bring each vehicle back to life. This diverse crew embodies the dual spirit of the company: honoring classic design while building with the precision of modern technology. Their collective expertise ensures that every Bronco, VW Bus or other special project rolling off the line is equal parts heritage and innovation.

Branding and Design

The Kindred Motorworks logo captures the spirit of the company in a single handwritten wordmark. Its flowing, script-style typography feels personal and approachable, like a signature, hinting at the hands-on craftsmanship that goes into every build. Beneath it, the small, sans-serif “Motorworks” grounds the mark in modernity, balancing nostalgia with clarity. Together, they communicate the company’s mission to merge classic design with future-ready performance, keeping the brand firmly in the driver’s seat.

Branding across their website and materials leans into simplicity and trust, echoing the uncluttered geometry of the Ford Bronco and VW Bus. The identity doesn’t shout; it reflects confidence in craftsmanship and the timelessness of the cars themselves.

A Modern Classic

What sets Kindred apart isn’t just the vehicles, it’s the vision of reviving icons while respecting both form and function. Their Mare Island headquarters hums with activity, but beneath the buzz lies a philosophy of design: one that sees the past not as something to preserve in amber, but as something to reimagine for today. Kindred reminds us that the cars we love can be timeless, if treated with care, imagination, and a touch of modern engineering magic.

Explore Kindred Motorworks →

Original Source: https://smashingmagazine.com/2025/10/ai-ux-achieve-more-with-less/

I have made a lot of mistakes with AI over the past couple of years. I have wasted hours trying to get it to do things it simply cannot do. I have fed it terrible prompts and received terrible output. And I have definitely spent more time fighting with it than I care to admit.

But I have also discovered that when you stop treating AI like magic and start treating it like what it actually is (a very enthusiastic intern with zero life experience), things start to make more sense.

Let me share what I have learned from working with AI on real client projects across user research, design, development, and content creation.

How To Work With AI

Here is the mental model that has been most helpful for me. Treat AI like an intern with zero experience.

An intern fresh out of university has lots of enthusiasm and qualifications, but no real-world experience. You would not trust them to do anything unsupervised. You would explain tasks in detail. You would expect to review their work multiple times. You would give feedback and ask them to try again.

This is exactly how you should work with AI.

The Basics Of Prompting

I am not going to pretend to be an expert. I have just spent way too much time playing with this stuff because I like anything shiny and new. But here is what works for me.

Define the role.
Start with something like “Act as a user researcher” or “Act as a copywriter.” This gives the AI context for how to respond.
Break it into steps.
Do not just say “Analyze these interview transcripts.” Instead, say “I want you to complete the following steps. One, identify recurring themes. Two, look for questions users are trying to answer. Three, note any objections that come up. Four, output a summary of each.”
Define success.
Tell it what good looks like. “I am looking for a report that gives a clear indication of recurring themes and questions in a format I can send to stakeholders. Do not use research terminology because they will not understand it.”
Make it think.
Tell it to think deeply about its approach before responding. Get it to create a way to test for success (known as a rubric) and iterate on its work until it passes that test.

Here is a real prompt I use for online research:

Act as a user researcher. I would like you to carry out deep research online into [brand name]. In particular, I would like you to focus on what people are saying about the brand, what the overall sentiment is, what questions people have, and what objections people mention. The goal is to create a detailed report that helps me better understand the brand perception.

Think deeply about your approach before carrying out the research. Create a rubric for the report to ensure it is as useful as possible. Keep iterating until the report scores extremely high on the rubric. Only then, output the report.

That second paragraph (the bit about thinking deeply and creating a rubric), I basically copy and paste into everything now. It is a universal way to get better output.

Learn When To Trust It

You should never fully trust AI. Just like you would never fully trust an intern you have only just met.

To begin with, double-check absolutely everything. Over time, you will get a sense of when it is losing its way. You will spot the patterns. You will know when to start a fresh conversation because the current one has gone off the rails.

But even after months of working with it daily, I still check its work. I still challenge it. I still make it cite sources and explain its reasoning.

The key is that even with all that checking, it is still faster than doing it yourself. Much faster.

Using AI For User Research

This is where AI has genuinely transformed my work. I use it constantly for five main things.

Online Research

I love AI for this. I can ask it to go and research a brand online. What people are saying about it, what questions they have, what they like, and what frustrates them. Then do the same for competitors and compare.

This would have taken me days of trawling through social media and review sites. Now it takes minutes.

I recently did this for an e-commerce client. I wanted to understand what annoyed people about the brand and what they loved. I got detailed insights that shaped the entire conversion optimization strategy. All from one prompt.

Analyzing Interviews And Surveys

I used to avoid open-ended questions in surveys. They were such a pain to review. Now I use them all the time because AI can analyze hundreds of text responses in seconds.

For interviews, I upload the transcripts and ask it to identify recurring themes, questions, and requests. I always get it to quote directly from the transcripts so I can verify it is not making things up.

The quality is good. Really good. As long as you give it clear instructions about what you want.

Making Sense Of Data

I am terrible with spreadsheets. Put me in front of a person and I can understand them. Put me in front of data, and my eyes glaze over.

AI has changed that. I upload spreadsheets to ChatGPT and just ask questions. “What patterns do you see?” “Can you reformat this?” “Show me this data in a different way.”

Microsoft Clarity now has Copilot built in, so you can ask it questions about your analytics data. Triple Whale does the same for e-commerce sites. These tools are game changers if you struggle with data like I do.

Research Projects

This is probably my favorite technique. In ChatGPT and Claude, you can create projects. In other tools, they are called spaces. Think of them as self-contained folders where everything you put in is available to every conversation in that project.

When I start working with a new client, I create a project and throw everything in. Old user research. Personas. Survey results. Interview transcripts. Documentation. Background information. Site copy. Anything I can find.

Then I give it custom instructions. Here is one I use for my own business:

Act as a business consultant and marketing strategy expert with good copywriting skills. Your role is to help me define the future of my UX consultant business and better articulate it, especially via my website. When I ask for your help, ask questions to improve your answers and challenge my assumptions where appropriate.

I have even uploaded a virtual board of advisors (people I wish I had on my board) and asked AI to research how they think and respond as they would.

Now I have this project that knows everything about my business. I can ask it questions. Get it to review my work. Challenge my thinking. It is like having a co-worker who never gets tired and has a perfect memory.

I do this for every client project now. It is invaluable.

Creating Personas

AI has reinvigorated my interest in personas. I had lost heart in them a bit. They took too long to create, and clients always said they already had marketing personas and did not want to pay to do them again.

Now I can create what I call functional personas. Personas that are actually useful to people who work in UX. Not marketing fluff about what brands people like, but real information about what questions they have and what tasks they are trying to complete.

I upload all my research to a project and say:

Act as a user researcher. Create a persona for [audience type]. For this persona, research the following information: questions they have, tasks they want to complete, goals, states of mind, influences, and success metrics. It is vital that all six criteria are addressed in depth and with equal vigor.

The output is really good. Detailed. Useful. Based on actual data rather than pulled out of thin air.

Here is my challenge to anyone who thinks AI-generated personas are somehow fake. What makes you think your personas are so much better? Every persona is a story of a hypothetical user. You make judgment calls when you create personas, too. At least AI can process far more information than you can and is brilliant at pattern recognition.

My only concern is that relying too heavily on AI could disconnect us from real users. We still need to talk to people. We still need that empathy. But as a tool to synthesize research and create reference points? It is excellent.

Using AI For Design And Development

Let me start with a warning. AI is not production-ready. Not yet. Not for the kind of client work I do, anyway.

Three reasons why:

It is slow if you want something specific or complicated.
It can be frustrating because it gets close but not quite there.
And the quality is often subpar. Unpolished code, questionable design choices, that kind of thing.

But that does not mean it is not useful. It absolutely is. Just not for final production work.

Functional Prototypes

If you are not too concerned with matching a specific design, AI can quickly prototype functionality in ways that are hard to match in Figma. Because Figma is terrible at prototyping functionality. You cannot even create an active form field in a Figma prototype. It’s the biggest thing people do online other than click links — and you cannot test it.

Tools like Relume and Bolt can create quick functional mockups that show roughly how things work. They are great for non-designers who just need to throw together a prototype quickly. For designers, they can be useful for showing developers how you want something to work.

But you can spend ages getting them to put a hamburger menu on the right side of the screen. So use them for quick iteration, not pixel-perfect design.

Small Coding Tasks

I use AI constantly for small, low-risk coding work. I am not a developer anymore. I used to be, back when dinosaurs roamed the earth, but not for years.

AI lets me create the little tools I need. A calculator that calculates the ROI of my UX work. An app for running top task analysis. Bits of JavaScript for hiding elements on a page. WordPress plugins for updating dates automatically.

Just before running my workshop on this topic, I needed a tool to create calendar invites for multiple events. All the online services wanted £16 a month. I asked ChatGPT to build me one. One prompt. It worked. It looked rubbish, but I did not care. It did what I needed.

If you are a developer, you should absolutely be using tools like Cursor by now. They are invaluable for pair programming with AI. But if you are not a developer, just stick with Claude or Bolt for quick throwaway tools.

Reviewing Existing Services

There are some great tools for getting quick feedback on existing websites when budget and time are tight.

If you need to conduct a UX audit, Wevo Pulse is an excellent starting point. It automatically reviews a website based on personas and provides visual attention heatmaps, friction scores, and specific improvement recommendations. It generates insights in minutes rather than days.

Now, let me be clear. This does not replace having an experienced person conduct a proper UX audit. You still need that human expertise to understand context, make judgment calls, and spot issues that AI might miss. But as a starting point to identify obvious problems quickly? It is a great tool. Particularly when budget or time constraints mean a full audit is not on the table.

For e-commerce sites, Baymard has UX Ray, which analyzes flaws based on their massive database of user research.

Checking Your Designs

Attention Insight has taken thousands of hours of eye-tracking studies and trained AI on it to predict where people will look on a page. It has about 90 to 96 percent accuracy.

You upload a screenshot of your design, and it shows you where attention is going. Then you can play around with your imagery and layout to guide attention to the right place.

It is great for dealing with stakeholders who say, “People won’t see that.” You can prove they will. Or equally, when stakeholders try to crowd the interface with too much stuff, you can show them attention shooting everywhere.

I use this constantly. Here is a real example from a pet insurance company. They had photos of a dog, cat, and rabbit for different types of advice. The dog was far from the camera. The cat was looking directly at the camera, pulling all the attention. The rabbit was half off-frame. Most attention went to the cat’s face.

I redesigned it using AI-generated images, where I could control exactly where each animal looked. Dog looking at the camera. Cat looking right. Rabbit looking left. All the attention drawn into the center. Made a massive difference.

Creating The Perfect Image

I use AI all the time for creating images that do a specific job. My preferred tools are Midjourney and Gemini.

I like Midjourney because, visually, it creates stunning imagery. You can dial in the tone and style you want. The downside is that it is not great at following specific instructions.

So I produce an image in Midjourney that is close, then upload it to Gemini. Gemini is not as good at visual style, but it is much better at following instructions. “Make the guy reach here” or “Add glasses to this person.” I can get pretty much exactly what I want.

The other thing I love about Midjourney is that you can upload a photograph and say, “Replicate this style.” This keeps consistency across a website. I have a master image I use as a reference for all my site imagery to keep the style consistent.

Using AI For Content

Most clients give you terrible copy. Our job is to improve the user experience or conversion rate, and anything we do gets utterly undermined by bad copy.

I have completely stopped asking clients for copy since AI came along. Here is my process.

Build Everything Around Questions

Once I have my information architecture, I get AI to generate a massive list of questions users will ask. Then I run a top task analysis where people vote on which questions matter most.

I assign those questions to pages on the site. Every page gets a list of the questions it needs to answer.

Get Bullet Point Answers From Stakeholders

I spin up the content management system with a really basic theme. Just HTML with very basic formatting. I go through every page and assign the questions.

Then I go to my clients and say: “I do not want you to write copy. Just go through every page and bullet point answers to the questions. If the answer exists on the old site, copy and paste some text or link to it. But just bullet points.”

That is their job done. Pretty much.

Let AI Draft The Copy

Now I take control. I feed ChatGPT the questions and bullet points and say:

Act as an online copywriter. Write copy for a webpage that answers the question [question]. Use the following bullet points to answer that question: [bullet points]. Use the following guidelines: Aim for a ninth-grade reading level or below. Sentences should be short. Use plain language. Avoid jargon. Refer to the reader as you. Refer to the writer as us. Ensure the tone is friendly, approachable, and reassuring. The goal is to [goal]. Think deeply about your approach. Create a rubric and iterate until the copy is excellent. Only then, output it.

I often upload a full style guide as well, with details about how I want it to be written.

The output is genuinely good. As a first draft, it is excellent. Far better than what most stakeholders would give me.

Stakeholders Review And Provide Feedback

That goes into the website, and stakeholders can comment on it. Once I get their feedback, I take the original copy and all their comments back into ChatGPT and say, “Rewrite using these comments.”

Job done.

The great thing about this approach is that even if stakeholders make loads of changes, they are making changes to a good foundation. The overall quality still comes out better than if they started with a blank sheet.

It also makes things go smoother because you are not criticizing their content, where they get defensive. They are criticizing AI content.

Tools That Help

If your stakeholders are still giving you content, Hemingway Editor is brilliant. Copy and paste text in, and it tells you how readable and scannable it is. It highlights long sentences and jargon. You can use this to prove to clients that their content is not good web copy.

If you pay for the pro version, you get AI tools that will rewrite the copy to be more readable. It is excellent.

What This Means for You

Let me be clear about something. None of this is perfect. AI makes mistakes. It hallucinates. It produces bland output if you do not push it hard enough. It requires constant checking and challenging.

But here is what I know from two years of using this stuff daily. It has made me faster. It has made me better. It has freed me up to do more strategic thinking and less grunt work.

A report that would have taken me five days now takes three hours. That is not an exaggeration. That is real.

Overall, AI probably gives me a 25 to 33 percent increase in what I can do. That is significant.

Your value as a UX professional lies in your ideas, your questions, and your thinking. Not your ability to use Figma. Not your ability to manually review transcripts. Not your ability to write reports from scratch.

AI cannot innovate. It cannot make creative leaps. It cannot know whether its output is good. It cannot understand what it is like to be human.

That is where you come in. That is where you will always come in.

Start small. Do not try to learn everything at once. Just ask yourself throughout your day: Could I do this with AI? Try it. See what happens. Double-check everything. Learn what works and what does not.

Treat it like an enthusiastic intern with zero life experience. Give it clear instructions. Check its work. Make it try again. Challenge it. Push it further.

And remember, it is not going to take your job. It is going to change it. For the better, I think. As long as we learn to work with it rather than against it.

Original Source: https://www.sitepoint.com/audit-accessibility-with-wave/?utm_source=rss

Audit your website’s accessibility with WAVE Extension. Learn to detect missing alt text, contrast errors, and ARIA issues before going live.

Continue reading
How to Audit Web Accessibility with WAVE Extension
on SitePoint.

Original Source: https://smashingmagazine.com/2025/10/how-make-ux-research-hard-to-ignore/

In the early days of my career, I believed that nothing wins an argument more effectively than strong and unbiased research. Surely facts speak for themselves, I thought.

If I just get enough data, just enough evidence, just enough clarity on where users struggle — well, once I have it all and I present it all, it alone will surely change people’s minds, hearts, and beliefs. And, most importantly, it will help everyone see, understand, and perhaps even appreciate and commit to what needs to be done.

Well, it’s not quite like that. In fact, the stronger and louder the data, the more likely it is to be questioned. And there is a good reason for that, which is often left between the lines.

Research Amplifies Internal Flaws

Throughout the years, I’ve often seen data speaking volumes about where the business is failing, where customers are struggling, where the team is faltering — and where an urgent turnaround is necessary. It was right there, in plain sight: clear, loud, and obvious.

But because it’s so clear, it reflects back, often amplifying all the sharp edges and all the cut corners in all the wrong places. It reflects internal flaws, wrong assumptions, and failing projects — some of them signed off years ago, with secured budgets, big promotions, and approved headcounts. Questioning them means questioning authority, and often it’s a tough path to take.

As it turns out, strong data is very, very good at raising uncomfortable truths that most companies don’t really want to acknowledge. That’s why, at times, research is deemed “unnecessary,” or why we don’t get access to users, or why loud voices always win big arguments.

So even if data is presented with a lot of eagerness, gravity, and passion in that big meeting, it will get questioned, doubted, and explained away. Not because of its flaws, but because of hope, reluctance to change, and layers of internal politics.

This shows up most vividly in situations when someone raises concerns about the validity and accuracy of research. Frankly, it’s not that somebody is wrong and somebody is right. Both parties just happen to be right in a different way.

What To Do When Data Disagrees

We’ve all heard that data always tells a story. However, it’s never just a single story. People are complex, and pointing out a specific truth about them just by looking at numbers is rarely enough.

When data disagrees, it doesn’t mean that either is wrong. It’s just that different perspectives reveal different parts of a whole story that isn’t completed yet.

In digital products, most stories have 2 sides:

Quantitative data ← What/When: behavior patterns at scale.
Qualitative data ← Why/How: user needs and motivations.
↳ Quant usually comes from analytics, surveys, and experiments.
↳ Qual comes from tests, observations, and open-ended surveys.

Risk-averse teams overestimate the weight of big numbers in quantitative research. Users exaggerate the frequency and severity of issues that are critical for them. As Archana Shah noted, designers get carried away by users’ confident responses and often overestimate what people say and do.

And so, eventually, data coming from different teams paints a different picture. And when it happens, we need to reconcile and triangulate. With the former, we track what’s missing, omitted, or overlooked. With the latter, we cross-validate data — e.g., finding pairings of qual/quant streams of data, then clustering them together to see what’s there and what’s missing, and exploring from there.

And even with all of it in place and data conflicts resolved, we still need to do one more thing to make a strong argument: we need to tell a damn good story.

Facts Don’t Win Arguments, Stories Do

Research isn’t everything. Facts don’t win arguments — powerful stories do. But a story that starts with a spreadsheet isn’t always inspiring or effective. Perhaps it brings a problem into the spotlight, but it doesn’t lead to a resolution.

The very first thing I try to do in that big boardroom meeting is to emphasize what unites us — shared goals, principles, and commitments that are relevant to the topic at hand. Then, I show how new data confirms or confronts our commitments, with specific problems we believe we need to address.

When a question about the quality of data comes in, I need to show that it has been reconciled and triangulated already and discussed with other teams as well.

A good story has a poignant ending. People need to see an alternative future to trust and accept the data — and a clear and safe path forward to commit to it. So I always try to present options and solutions that we believe will drive change and explain our decision-making behind that.

They also need to believe that this distant future is within reach, and that they can pull it off, albeit under a tough timeline or with limited resources.

And: a good story also presents a viable, compelling, shared goal that people can rally around and commit to. Ideally, it’s something that has a direct benefit for them and their teams.

These are the ingredients of the story that I always try to keep in my mind when working on that big presentation. And in fact, data is a starting point, but it does need a story wrapped around it to be effective.

Wrapping Up

There is nothing more disappointing than finding a real problem that real people struggle with and facing the harsh reality of research not being trusted or valued.

We’ve all been there before. The best thing you can do is to be prepared: have strong data to back you up, include both quantitative and qualitative research — preferably with video clips from real customers — but also paint a viable future which seems within reach.

And sometimes nothing changes until something breaks. And at times, there isn’t much you can do about it unless you are prepared when it happens.

“Data doesn’t change minds, and facts don’t settle fights. Having answers isn’t the same as learning, and it for sure isn’t the same as making evidence-based decisions.”

— Erika Hall

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Useful Resources

“How to Present Research So Stakeholders Sit Up and Take Action”, by Nikki Anderson
“What To Do When Data Disagrees”, by Subhasree Chatterjee, Archana Shah, Sanket Shukl, and Jason Bressler
“Mixed-Method UX Research”, by Raschin Fatemi
“A Step-by-Step Framework For Mixed-Method Research”, by Jeremy Williams
“The Ultimate Guide To Mixed Methods”, by Ben Wiedmaier
Survey Design Cheatsheet, by yours truly
Useful Calculators For UX Research, by yours truly
Beyond Measure, by Erika Hall

Useful Books

Just Enough Research, by Erika Hall
Designing Surveys That Work, by Caroline Jarrett
Designing Quality Survey Questions, by Sheila B. Robinson

Original Source: https://webdesignerdepot.com/exciting-new-tools-for-designers-september-2025/

Web design tools that use artificial intelligence are still dominating new and beta launches, while hopefully making your workflows a little bit smoother. One of the most talked about launches this month is Google (Gemini’s) Nano Banana image generator tool, which has even been the subject of the viral AI saree prompt. Have you tried […]