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As I watched the new series, I only cared about Piggy — the thoughtful, smart kid stranded on an island with other boys. That made me think about what we look for in art.

(Image credit: J Redza)

When the Food and Drug Administration approved Gleevec to treat a form of leukemia in 2001, it ushered in a new era in cancer care.

(Image credit: Kristyna Wentz-Graff)

This article is a sponsored by Penpot

Imagine that your Penpot file contains a full icon set in addition to the design itself, which uses some but not all of those icons. If you were to ask an AI such as Claude or Gemini to export only the icons that are being used, it wouldn’t be able to do that. It’s not able to interact with Penpot files.

However, a Penpot MCP server can. It can perform a handpicked number of operations under set rules and permissions, especially since Penpot has an extensive API and even more so because it’s open-source.

The AI’s job is simply to understand your intent, choose the right operation for the MCP server to perform (an export in this case), and pass along any parameters (i.e., icons that are being used). The MCP server then translates this into a structured API request and executes it.

It might help to think of AI as a server in a restaurant that takes your order, the MCP server as both the menu and chef, and the API request as (hopefully) a hot pizza pie on a warm plate.

Why MCP servers, exactly? Well, Penpot isn’t able to understand your intent because it’s not an LLM, nor does it allow third-party LLMs to interact with your Penpot files for the security and privacy of your Penpot data. Although Penpot MCP servers do act as a secure bridge, translating AI intent into API requests using your Penpot files and data as context.

What’s even better is that because Penpot takes a design-expressed-as-code approach, designs can be programmatically created, edited, and analyzed on a granular level. It’s more contextual, more particular, and therefore more powerful in comparison to what other MCP servers offer, and far more thoughtful than the subpar ‘Describe → Generate’ AI workflow that I don’t think anybody really wants. Penpot’s AI whitepaper describes this as the bad approach and the ‘Convert to Code’ approach as the ugly approach, whereas MCP servers are more refined and adaptable.

Features And Technical Details

Before we move on to use cases, here are some features and technical details that further explain how Penpot MCP servers work:

• Complies with MCP standards;
• Integrates with the Penpot API for real-time design data;
• Includes a Python SDK, REST API, plugin system, and CLI tools;
• Works with any MCP-enabled AI assistant (Claude in VS Code, Claude in Cursor, Claude Desktop, etc.);
• Supports sharing design context with AI models, and letting them see and understand components;
• Facilitates communication with Penpot using natural language.

What, then, could MCP servers enable us to do in Penpot, and what have existing experiments already achieved? Let’s take a look.

Penpot MCP Server Use-Cases

If you just want to skip to what Penpot MCP servers can do, Penpot have a few MCP demos stashed in a Google Drive that are more than worth watching. Penpot CEO Pablo Ruiz-Múzquiz mentioned that videos 03, 04, 06, 08, and 12 are their favorites.

An even faster way to summarize MCP servers is to watch the unveiling from Penpot Fest 2025.

Otherwise, let’s take a look at some of the more refined examples that Penpot demonstrated in their public showcase.

Design-to-Code and Back Again (and More)

Running on from what I was saying earlier about how Penpot designs are expressed as code, this means that MCP servers can be used to convert design to code using AI, but also code to design, design to documentation, documentation to design system elements, design to code again based on said design system, and then completely new components based on said design system.

It sounds surreal, but the demo below shows off this transmutability, and it’s not from vague instruction but rather previous design choices, regardless of how they were expressed (design, code, or documentation). There are no surprises — these are simply the decisions that you would’ve made anyway based on previous decisions, executed swiftly.

In the demo, Juan de la Cruz García, Designer at Penpot, frictionlessly transmutes some simple components into documentation, design system elements, code, new components, and even a complete Storybook project like a piece of Play-Doh:

Design-to-Code, Design/Code Validation, And Simple Operations

In a similar demo below, Dominik Jain, Co-Founder at Oraios AI, creates a Node.js web app based on the design before updating the frontend styles, saves names and identifiers to memory to ensure smooth design-to-code translation before checking it for consistency, adds a comment next to the selected shape in Penpot, and then replaces a scribble in Penpot with an adapted component. There’s a lot happening here, but you can see exactly what Dominik is typing into Claude Desktop as well as Claude’s responses, and it’s very robust:

By the way, the previous demo used Claude in VS Code, so I should note that Penpot MCP servers are LLM-agnostic. Your tech stack is totally up to you. IvanTheGeek managed to set up their MCP server with the JetBrains Rider IDE and Junie AI.

More Use Cases

Translate a Penpot board to production-ready semantic HTML and modular CSS while leveraging any Penpot design tokens (remember that Penpot designs are already expressed as code, so this isn’t a shot in the dark):

Generate an interactive web prototype without changing the existing HTML:

As shown earlier, convert a scribble into a component, leveraging existing design and/or design system elements:

Create design system documentation from a Penpot file:

And here are some more use-cases from Penpot and the community:

• Advanced exports,
• Search for design elements using natural language,
• Pull data from external APIs using natural language,
• Easily connect Penpot to other external tools,
• Saving repetitive tasks to memory and executing them,
• Visual regression testing,
• Design consistency and redundancy checking,
• Accessibility and usability analysis and feedback,
• Design system compliance checking,
• Guideline compliance checking (brand, content, etc.),
• Monitor adoption and usage with design analytics,
• Automatically keep documentation in sync with design,
• Design file organization (e.g., tagging/categorization).

Essentially, Penpot MCP servers lead the way to an infinite number of workflows thanks to the efficiency and ease of your chosen LLM/LLM client, but without exposing your data to it.

What Would You Use MCP Servers For?

Penpot MCP servers aren’t even at the beta stage, but it is an active experiment that you can be a part of. Penpot users have already begun exploring use cases for MCP servers, but Penpot wants to see more. To ensure that the next generation of design tools meets the needs of designers, developers, and product teams in general, they must be built collectively and collaboratively, especially where AI is concerned.

Note: Penpot is looking for beta testers eager to explore, experiment with, and help refine Penpot’s MCP Server. To join, write to support@penpot.app with the subject line “MCP beta test volunteer.”

Is there anything that you feel Penpot MCP servers could do that current tools aren’t able to do well enough, fast enough, or aren’t able to do at all?

You can learn how to set up a Penpot MCP server right here and start tinkering today, or if your brain’s buzzing with ideas already, Penpot want you to join the discussion, share your feedback, and talk about your use-cases. Alternatively, the comment section right below isn’t a bad place to start either!

Has work felt “different” to you? You show up, do your work, fix what needs fixing, and get the job done, but the excitement isn’t quite the same anymore. Maybe the work has become too routine, or maybe you’ve grown in a way your role hasn’t kept up with. You catch yourself thinking, “I’ve been doing this for years, but where do I go from here?”

It’s not always about the burnouts or frustrations. Sometimes it’s just curiosity. You’ve learned a lot, built things, solved problems, and now a small part of you wants to see what else you can do. Maybe the rise of AI is making you look at your job differently, or maybe you feel ready for a new kind of challenge that does not look like your current day-to-day.

I have seen many people across different fields go through this. Developers moving into product work, designers shifting to UX research, engineers getting into teaching, or support folks building communities. Everyone reaches that point where they want their work to feel meaningful again.

The good thing is you are not starting from zero. The experience you already have, like solving problems, making decisions, working, and communicating with people, those are real, valuable skills that carry over anywhere. Most of the time, the next step is not about leaving tech behind. It’s about finding where your skills make the most sense next.

This article is about that: How to rethink your path when things start to feel a bit stale, and how to move toward something new without losing everything you’ve built so far.

Redefining Your Toolkit

When people start thinking about changing careers, the first thing they usually do is focus on what they do not have. The missing skills, the new tools they need to learn, or how far behind they feel. It is a normal reaction, but it is not always the best place to begin.

Instead, try looking at what is already there. You have probably built more useful skills than you realize. Many of us get used to describing ourselves by our job titles, such as developer, designer, or analyst, but those titles do not fully explain what we actually do. They just tell us where we sit on a team. The real story is the work behind the title.

Think of a developer, for example. On paper, the job is to write code, but in reality, a developer spends most of their time solving problems, making decisions, and building systems that make sense to other people. The same goes for designers. They do not just make things look good; they pay attention to how people think, how they move through a screen, and how to make something feel clear and simple.

Your skills don’t disappear when your title changes. They just find new ways to show up.

These are what people call transferable skills, but you do not need the fancy term to get the idea. These are abilities that stay useful no matter where you go. Problem-solving, curiosity, clear communication, empathy, and learning fast — these are the things that make you good at what you do, even if the tools or roles change.

You already use them more than you think. When you fix a bug, you are learning how to track a problem back to its roots. When you explain a technical idea to someone non-technical, you are practicing clarity. When you deal with tight deadlines, you are learning how to manage priorities. None of these disappear if you switch fields. You apply it somewhere else.

So, before you worry about what you do not know, take a moment to see what you already do well. Write it down if you have to. Not just the tasks, but the thinking behind them. That is where your real value is.

Four Real-World Paths to Explore

Once you start seeing your skills beyond your job title, you may realize how many directions you can actually take. The tech world keeps changing fast: tools change, teams change, new roles show up every year, and people move in ways they never planned.

Here are four real paths that many people in tech are taking today.

From To What Changes Why It Works Developer Product Manager You move from building the product to shaping what gets built and why. Developers already understand tradeoffs, user needs, and how features come together. That is product thinking in action. Engineer Developer Advocate You focus less on code delivery and more on helping others succeed with your product. You already know the technology inside out, so turning that knowledge into clear communication makes you a natural teacher. Back-end Engineer Solutions Engineer You bring your problem-solving mindset to real client challenges. It is not about selling, it is about understanding problems deeply and building trust through technical skill. Designer UX Researcher or Service Designer You shift from visuals to understanding how people think, feel, and interact. Good design starts with empathy, and that same skill fits perfectly in research and experience design.

What many people discover when they take one of these steps is that their daily work changes, not their identity. The tools and routines might be different, but the core way they think and solve problems stays the same.

The biggest change is usually perspective. Instead of focusing on how something gets built, you begin to care more about why it matters, who it helps, and what impact it has. For many people, that shift often brings back the excitement they might have lost somewhere along the way.

Your First Steps Towards A New Path

When you find a direction that feels interesting, the next step is figuring out how to move toward it without losing your footing where you are. This is where curiosity turns into a plan.

1. Take A Look At What You Bring

Start by checking your strengths. It does not have to be anything complex. Write down what you do well, what feels natural to you, and what people usually ask you for help with.

If you want a simple guide, Learning People has a good breakdown for auditing your personal skills, including a template for identifying and evaluating your skills. Try filling it out; it’s well worth the few minutes it takes to complete.

After listing your strengths, try matching them with roles you’re curious about. For example, if you’re a developer who enjoys explaining things, that could connect well with mentoring, writing tutorials, or developer advocacy.

2. Learn By Getting Close To It

Job descriptions aren’t a perfect reflection of the realities of working a specific job. Talking with people who do that job will. So, reach out to people who already do what you’re interested in and ask them what their day-to-day looks like, what parts they enjoy, and what surprised them when they started.

And if possible, shadow someone or volunteer to help on a project. You don’t need a job change to explore something new. Short, hands-on experiences often teach you far more than any course, and many people are more than willing to take you under their wing, especially if you are offering your time and help in exchange for experience.

3. Build Proof Through Small Experiments

Do something small that points in the direction you want to go. Maybe build a simple tool, write a short piece about what you’re learning, or help a local startup or open-source team. These don’t need to be perfect, but they just need to exist. They show direction, not completion.

Blogging has always been a perfect way to share your learning path and demonstrate your excitement about it. Plus, it establishes a track record of the knowledge you acquire.

4. Shape Your Story As You Grow

Instead of going with the idea of “I’m switching careers,” try thinking of it as “I’m building on what I already do.” That simple shift makes your journey clearer. It shows that you’re not starting from zero — you’re simply moving forward with more intention.

Navigating The Mental Hurdles

Every career shift, even when it feels exciting, comes with doubts. You might ask yourself, “What if I’m not ready?” or “What if I can’t keep up?” These thoughts are more common than people admit.

Imposter Syndrome

One fear that shows up a lot is imposter syndrome, that feeling you do not belong or that others are “better” or “smarter” at something than you. A recent piece from Nordcloud shared that more than half (58%) of IT professionals have felt this at some point in their career.

Comparison is a silent thief of confidence. Seeing others move faster can make you feel late. But everyone has different opportunities and different timing. What matters is the direction you are moving in, not how fast you go.

Here’s a thought worth remembering:

People who have successfully changed their careers did not wait until they felt brave. Most of them still had doubts, but they just moved anyway, one small step at a time. Starting Again

Another worry is the idea of starting over. You may feel that you’ve spent too many years in one space to move into another. But you are not returning to the beginning. You are moving with experience. Your habits, discipline, and problem-solving stay with you. They just show up in a different way.

It’s hard — and self-defeating — to imagine the work it takes to start all over again, especially when you have invested many years into what you do. But remember, it’s not always too late. Even Kurt Vonnegut was 47 when he wrote his seminal book, Slaughterhouse Five. You can still enjoy a very long and fruitful career, even in middle age.

Finances

Money and stability also weigh a lot. The fear of losing income or looking uncertain can hold you back. And everyone’s money situation can be wildly different. You may have family to support, big loans to pay back, a lack of reserves, or any number of completely valid reasons for not wanting to give up a steady paycheck when you’re already receiving one.

A simple way to reduce that pressure is to start with small steps. Take a small side gig, try part-time work, or help on a short project in the area you’re curious about. These small tests give you clarity without shaking your foundation.

Conversations With Industry Experts

Below are short interviews with a handful of tech professionals serving in different roles. I wanted to talk with real people who have recently switched careers or are in the process of doing so because it helps illustrate the wide range of situations, challenges, and opportunities you might expect to encounter in a career change.

Thomas Dodoo: Graphic Designer, 5 Years Of Experience

Background: Thomas has an IT background. He first got interested in tech through game development in school, but later discovered that design was what he enjoyed more. Over time, he moved fully into graphic design and branding.

Question: When you were starting, what confused you the most about choosing your path?

Thomas: I wasn’t sure if I should stay with game development or follow design. I liked both, but design came more naturally, so I just kept learning little by little.

Question: Was there a moment that made you take your design work more seriously?

Thomas: Yes, the first time someone trusted me with their full brand. It made me realise this could be more than a hobby.

Question: What skills did you carry over from development into your design work?

Thomas: My background in development helped me think more logically about design. I break things down, think in steps, and focus on how things work, not just how they look.

Adwoa Mensah: Product Manager, 4 Years Of Experience

Background: Adwoa moved from software testing to product management.

Question: When did you realize it was time to change careers?

Adwoa: I realised it when I started caring more about why things were being built, not just checking if they worked. I enjoyed asking questions, giving input, and thinking about the bigger picture, and testing alone started to feel limiting.

Question: What new skills did you need to learn to move into your new field?

Adwoa: I had to learn how to communicate better, especially with designers, developers, and stakeholders. I also worked on planning, prioritising work, and understanding users more deeply. I learned most of this by watching product managers I worked with, asking questions, reading, and slowly taking on more responsibility on real projects.

Konstantinos Tournas: AI Engineer

Background: Konstantinos started programming with zero experience. He had no technical background at first, but he developed a strong interest in artificial intelligence and worked his way into the field.

Question: What moments in your journey made you question yourself, and how did you move past them?

Konstantinos: There were many moments in my career journey when I doubted myself, mainly because I started completely from zero, with no programming background and no connections in the field. What helped me push through was the motivation I had to learn and my genuine love for artificial intelligence. Every time I questioned myself, I reminded myself where I started and how far I had come in such a short amount of time.

Question: When you feel pressure or doubt in your work, what helps you stay grounded?

Konstantinos: When I feel pressure or self-doubt, I usually take a walk in nature. It helps me clear my mind and think creatively about how I can improve my work. In programming, the work rarely stops when your shift ends; problems in the code follow you throughout the day, and overcoming them requires creativity. Walking helps me reset and return with better ideas.

Question: How do you deal with comparing yourself to others in your field?

Konstantinos: Even though I’m competitive by nature, I constantly try to learn from others in my field. I don’t like showing off; I prefer listening. I know I can become great at what I do, but that doesn’t happen overnight. Comparison can be healthy, as long as it pushes you to grow rather than discourages you.

Question: What would you say to someone who feels like they are not good enough to pursue the path they want?

Konstantinos: I started programming without a university degree and with an entirely different background. Patience and persistence truly are the keys to success; it might sound cliché, but they were precisely what helped me. In less than six months, with long hours of focused work, consistency, and determination, I managed to get hired for my dream job simply because I believed in myself and wanted it badly enough.

Yinjian Huang: Product Designer (AI, SaaS), 5 Years Of Experience

Background: Yinjian works in product design across AI, SaaS, and B2B products. Her work focuses on building early-stage products, shaping user experience, and working closely with engineering and product teams on AI-driven features.

Question: Looking back, what is one decision you made that you think others in your field could learn from?

Yinjian: Keep learning across disciplines: design, PM, AI, and engineering. The broader your fluency, the better you can design and reason holistically. Cross‑functional knowledge compounds and unlocks better product judgment.

Question: What do you wish you had known about handling stress, workload, or expectations earlier in your career?

Yinjian: Communicate early if the workload is too heavy or a deadline is at risk. Flag constraints, renegotiate scope, and make trade‑offs explicit. Early clarity beats late surprises.

Question: How do you evaluate whether a new opportunity or challenge is worth taking on?

Yinjian: I evaluate opportunities on three axes: the learning delta (skills I’ll gain), the people I’ll work with, and alignment with my interests.

Question: What advice would you give to someone who wants to grow in your field but feels stuck or unsure of where to start?

Yinjian: Growth can feel overwhelming at first because there’s so much to learn. Build a simple roadmap: start by making your craft solid, then expand adjacent skills. Find the best resources, practice relentlessly, and seek feedback on tight cycles. Momentum comes from small, consistent wins.

The Bottom Line

This whole piece is just a reminder that it’s fine to question where you are and want something different. Everyone hits that moment when things stop feeling exciting, and you start wondering what’s next. It doesn’t mean you’ve failed. It usually means you’re growing.

I wrote this because I’ve been in that space too, still figuring out what direction makes the most sense for me. So if you’re feeling stuck or unsure, I hope this gave you something useful. You don’t need to have everything sorted out right now. Just keep learning, stay curious, and take one small step at a time.

This summer a 40-year-old friend and brilliant software engineer, Brandon Wirtz, died due to colon cancer and my dad died of pancreatic cancer too. At first neither of their doctors diagnosed properly (Brandon was frequently getting sick and my dad kept having more and more problems). Ever since Brandon discovered his cancer, I’ve started taking healthcare more seriously, wondering if there’s a way to diagnose such diseases earlier.

Last week a new clinic, Prenuvo, opened near San Francisco, that promises to do just that by doing a full-body MRI scan (Magnetic Resonance Imaging). This is like a high-resolution X-ray machine except it doesn’t use radiation to make its images.

I was lucky enough to be one of the first to be scanned in its new location (it has been doing such scans for a decade up in Vancouver, Canada) by founder, Dr Raj Attariwala. Here I filmed the consultation with Dr. Raj right after my scans were done.

The process? You pay $2,500. You spend an hour inside an MRI machine. For me, it was a chance to hold perfectly still for an hour, while I listened to the machine whir and buzz around me. After the hour, it takes a few minutes to process your images and then you sit down with a doctor, like I did here.

Luckily for me I got a pretty clean bill of health but you can see this is a powerful diagnostic tool to help doctors find dozens of problems before they become untreatable. Everything from heart disease to a variety of cancers. You can see how Dr. Raj walks through my entire body, including my brain, looking for problems that I’ll need to work on. He did find one with me, my mom had a bad back, and it looks like I’ve been blessed with the same problems, and he told me to do exercises to strengthen my core muscles to minimize that problem in the future.

In talking with cofounder/CEO Andrew Lacy the company has developed its own MRI machine to do these scans. He told me that most other MRIs are used only for specific body parts, usually after a cancer or problem has already been found. Prenuvo, he told me, has modified the software running the MRI machine to do specialized full-body scans that other machines can’t do easily. Also, his team is using these images to build machine learning to assist the doctors in helping find various problems and, also, in its plans to scale this to more people over time (the San Francisco location has two scanners that can do two people an hour, the company has plans to open more locations and do more scans per hour, but that will need more AI work, and a training of doctors to look for problems when they are early, rather late-stage like they usually see).

For me it’s amazing to see inside your own body for the first time and the company gives its customers all scans on a mobile app that you can explore on your own time later. It also sends the scans to your primary-care physician, or to other doctors for second opinions.

You can learn more about this service at https://www.prenuvo.com.

Business of the future will need to be more predictive.

That’s what VMware’s Justin Murray, a long-time VMware employee told me here as he explained the latest in AI and Machine Learning that he’s seeing evolve.

The folks who run VMware’s huge conference, VMworld (happens September 29-October 1), got interested in me after reading my latest book, “The Infinite Retina,” which is how augmented reality and artificial intelligence, along with a few other technologies that I call Spatial Computing, will radically change seven industries over the next decade.

You see this predictive nature of AI in things like robots, autonomous cars, and, even, other things like Spotify, which uses AI to build playlists. That predicts what kind of music we would like to listen to. Autonomous cars predict the next action of both people and other cars on the streets. The AI inside is always trying to answer questions like: “will a child walking on the side of the road try to cross the street in front of us?” Properly predicting what is about to happen on the road is important and, I ask Murray, if that same predictive technology is what he’s thinking will be used elsewhere in businesses?

Murray says “yes,” but then goes a lot further, and predicts what some of the hot discussions will be at VMworld next week.

For instance:

1. Every major cloud provider, like Amazon’s Web Services, Microsoft’s Azure, or Google’s Cloud Services, is buying tons of NVidia’s powerful GPUs for their datacenters to support these new, predictive, AI services that businesses are starting to build.

2. The AI architecture and tooling stack that runs on these is seeing sizeable changes, and NVidia and VMware will make some announcements there next week.

3. Powerful new AI supercomputers are now being built because NVidia cards are being “connected together” in a powerful new way to make new workloads possible.

Why do I care, especially since usually I’m interested in new startups or consumer electronics gadgets?

Well, let’s walk through my life. Recently I got a June Oven. You put a piece of toast in it, or a piece of salmon, and it uses a small camera and an NVidia card inside, along with machine learning-based software, to automatically recognize what food I am trying to cook or bake. It’s magic. Plus, I never burn my toast anymore the way we used to because I often didn’t get the settings right.

Or, look at the new DJI Osmo 4 I used to do the intro to this video. On there is three little motors, and the instructions for how to move those motors is generated, in part, by machine learning that is constantly evaluating how best to steady my iPhone.

Finally, look at my Tesla. Murray told me that there’s more than a dozen AI-based systems running on that, and it drove most of the way to VMware’s headquarters in Palo Alto, CA, which makes my drives more relaxing (particularly in traffic where my car does all the stop-and-go duties) and safer.

Already AI has radically changed my life and most people in the industry say AI is just getting going. One reason VMware is compensating me to do this series of posts is because about a decade ago I was the first to see Siri, which was the first AI-based consumer application to come to market. My posts back then kicked off the AI age but a decade later AI has started deeply falling in price and is getting simpler to do, so it’s being used in a lot more new workloads.

You might not realize just who VMware is, but you probably use one of its services everyday, or, rather, the companies you deal with everyday use VMware to run their businesses. When I worked at Rackspace, a major cloud computing provider, for instance, we used VMware all over the place in our datacenters. “VM” stands for “Virtual Machine,” and VMware is the one that popularized that term for technology that can split up a physical computer into tons of “virtual” computers (or join them together with millions of other machines to build a supercomputer). Today that technology is used to do a bunch of things, from letting you manage your laptop better and run it more safely, to managing huge businesses, to soon managing new Spatial Computing infrastructure and devices (I wrote about such in my latest book).

All of this will be discussed at VMworld, which is a huge free virtual event, with more than 100,000 attendees and hundreds of sessions, covers not just what is happening here in AI, but across a range of technologies that businesses use everyday, from security-focused ones, to data-center-management focused ones. If you like this conversation, which is just one out of thousands of VMware employees or customers you will meet at VMworld, register for your free attendee badge here.

I don’t even need an AI to predict that you’ll find at least a few of the sessions out of the 900 offered useful for your business, see you on the 29th!