Andy White

Day 15 of 🚢 Ship 30 for 30!!

Here’s my Digital Brain inside of Typeshare.

Based on my data-driven niche selection, I am committing to writing more about Programming Fundamentals in the AI Era.

I Built an AI Community, Then Walked Away. Here's What I Learned.

Why I shut down my Skool community after just a few months, the regret that followed, and what the experience taught me about building an online business.

The Day I Realized Something Had to Change

I decided to shut down my Skool community on a nice, cool day in the first quarter of 2026. I was sitting in the sunroom where I live.

I had a lot on my mind.

For the previous few months, I'd been running a Skool community called AI Builder Lab. I created it to help people learn AI workflow automation using tools like Zapier, Make, and n8n.

On the surface, things weren't going terribly wrong.

I was gaining new members. People were interested in learning about AI and automation. And I was building something around a subject I had spent considerable time learning about.

But something didn't feel right.

Instead of feeling excited about my community's future, I felt mentally exhausted, stressed, and increasingly overwhelmed.

I was starting to dread keeping everything going.

And I kept asking myself one question:

Why am I still building something I no longer enjoy?

I'd started the community because I wanted to build an online business doing something I was interested in. But somewhere along the way, it had begun to feel like another job.

That wasn't what I wanted.

Sitting in that sunroom, I started to realize I might need to make a change.

Not because I had failed.

Not because the community wasn't growing.

But because I was beginning to understand that this wasn't the right business for me.

It would take a few more weeks before I finally shut everything down.

But looking back, that was the day I knew something had to change.

Why I Started AI Builder Lab

I've been interested in technology and software development for a long time.

I spent years working as a software developer before leaving the industry. Even after leaving professional software development, I kept exploring new AI and programming technologies and different ways to build things online.

When AI tools became more accessible, I naturally got interested.

I was especially drawn to AI workflow automation.

Tools like Zapier, Make, and n8n let me connect applications, automate repetitive tasks, and build workflows that could save people time.

I enjoyed learning how these systems worked.

And like many people exploring AI, I started thinking about the business opportunities.

I could turn what I was learning into something useful for other people.

Or I could build a community around AI automation, help beginners develop practical skills, and eventually turn it into a sustainable online business?

That's how AI Builder Lab came about.

My idea was simple.

I was going to help people use AI and automation tools like Zapier to solve real business problems by teaching them how to build workflows and feel more confident using Zapier.

At the time, it felt like a good opportunity.

AI was growing rapidly, automation was becoming more popular, and people were looking for ways to use these tools.

But I hadn't fully considered something.

Being interested in a subject doesn't necessarily mean you'll enjoy building and operating a business around it.

I liked learning about AI automation.

I liked experimenting with tools and building workflows.

But running a community took more than interest in the technology.

It meant showing up consistently, creating educational material, supporting members, and staying current with an industry that seemed to change every week.

That was where things began to get difficult.

When My Online Business Started Feeling Like a Job

One of the biggest reasons I wanted to build an online business was the freedom it could provide.

I wanted to work on projects I enjoyed, set my own schedule, and create something I could grow over time.

I didn't want to build another job for myself.

But that's exactly how running AI Builder Lab started to feel.

The problem wasn't necessarily the community itself.

It was the pressure I was putting on myself to keep up.

AI technology was moving incredibly fast. Still is.

Every week, there seemed to be another new tool, another major update, or another AI capability everyone was talking about.

Just when I thought I was getting comfortable with one technology, something new would come along.

Because my community focused on teaching AI automation, I felt like I needed to stay on top of it all.

I needed to understand the tools and know what was changing.

And I felt like I needed to keep learning so I could continue providing value to members.

The more I tried to keep up, the more overwhelmed I became.

So instead of enjoying the learning process, I started to fall behind.

This was especially frustrating because I already had a technical background.

I’d spent at least a decade learning and working with programming languages like C and C++, working with software, and figuring out new technologies.

But AI was different.

The pace of change was too fast.

And I made the mistake of thinking I needed to understand everything that was happening.

I was no longer learning simply because I was curious.

I was learning because it felt necessary.

That's a big difference.

When something you enjoy, like creating Zapier workflows, becomes something you feel obligated to do every day, it can quickly lose its appeal.

I also realized that running a membership community wasn't as simple as creating content and watching people join.

There was an ongoing responsibility to the people who had become members.

Even when I wasn't actively working on the community, it was still on my mind.

What should I create next?

What should I teach?

Am I providing enough value?

Am I keeping up with the technology?

These questions kept adding pressure.

Eventually, the excitement I felt when I started AI Builder Lab gave way to stress and mental exhaustion.

And I had to admit something to myself.

I wasn't enjoying the business I was building.

Making the Decision to Walk Away

I didn't decide to shut down AI Builder Lab overnight.

I spent several weeks thinking about it.

Part of me wanted to continue.

After all, I'd already invested months of time and effort into building the community.

I'd spent time learning the technology, developing the concept, and trying to attract members.

And people were joining.

I was considering walking away from something that was beginning to gain traction but thought I should give it more time, and maybe it would get easier.

I also asked myself, should I just keep pushing through the difficult period? Was quitting too early the right move?

Those thoughts made the decision harder.

When you're building an online business, there's no shortage of advice telling you to stay consistent, keep showing up, and never give up.

And that advice has value.

Most worthwhile projects require patience.

But I was beginning to realize that persistence isn't always the answer.

Sometimes you need to ask whether the thing you're working so hard to build is actually something you want.

I had to consider whether I wanted to spend the next six months or even the next few years running this type of business.

The more I thought about it, the clearer the answer became.

I didn't.

I really enjoy learning about AI technology, but I wasn’t enjoying the responsibility of running a community focused on keeping people up to date with it.

I wanted more flexibility in my life, like running a business on my terms. I also wanted to learn at my own pace.

And I wanted to pursue projects without feeling like I had to keep up with every new AI development.

After a few weeks of going back and forth, I decided.

I was going to shut down AI Builder Lab.

I wasn't sure what I wanted to do next.

I didn't have another business waiting to replace it.

All I knew was that I needed a break.

And at the time, that felt like enough of a reason.

The Relief and Regret That Followed

When I finally shut down the community, I felt relieved.

For the first time in months, I wasn't thinking about what content I needed to create or which AI tools I needed to learn next.

I could take a break.

No pressure to create tutorials.

No pressure to keep up with every new AI development.

And no pressure to keep building something I wasn't enjoying.

I stepped away from my online business for a few months.

I slowed down and spent more time relaxing and doing other things.

And for a while, it felt good.

But I'd be lying if I said I didn't experience some regret.

Even though I knew shutting down the community was probably the right decision for me, I still wondered what might have happened if I'd continued.

Would the community have continued growing and eventually become successful?

Those questions stayed with me for a few weeks.

I think that's one of the difficult things about building an online business.

I asked myself, did I make the right decision?

It’s easy to think about and imagine how things might have turned out. But those possibilities are just that. Possibilities.

I also had to remind myself that the time I'd invested wasn't necessarily wasted.

I'd learned more about AI automation.

I'd gained experience creating and managing an online community.

And I’d discovered something important about the kind of business I didn’t want to build.

The knowledge I gained helped me realize I could regret losing a potential opportunity, even if the community itself didn’t continue, without erasing everything I’d learned.

It simply meant I was choosing not to continue down a path that no longer felt right.

The Shiny Object Syndrome Trap

After shutting down AI Builder Lab, I spent a few months away from actively building an online business.

But I didn't completely disconnect from the online business world.

I continued watching YouTube videos.

And if you've spent much time watching business and AI content on YouTube, you probably know how easy it is to get pulled in different directions.

One video introduces a new AI tool.

Another explains how someone is building a successful one-person business.

Then another shows you how to make money with automation, digital products, software, newsletters, or some other business model.

Before long, you have a dozen interesting ideas and no clear direction.

That's where I found myself.

I was consuming information about different opportunities without committing to one.

Every new idea seemed interesting.

Every new technology seemed worth exploring.

And every new business model seemed like something I should consider.

I had fallen back into the shiny object syndrome trap.

Such irony. I’d stepped away from AI Builder Lab partly because I was overwhelmed by trying to keep up with technology.

Yet there I was, doing that very thing, and I wasn’t making any progress on anything of my own.

And that led me to another realization.

I wasn’t building anything.

I wasn’t building anything. I was confusing watching Zapier workflow videos with making progress.

It felt productive. But unless you eventually choose a direction and take action, you’re mostly collecting information.

I also realized that shiny object syndrome wasn't my only problem.

I lacked focus.

I didn't have a clear idea of what I wanted to build, who I wanted to help, or what kind of work I wanted to spend my time doing.

Without that clarity, almost every new opportunity looked appealing.

Without a clear direction, it was hard to know which opportunities deserved my attention.

The more information I consumed, the more possibilities I discovered.

The more possibilities I discovered, the harder it became to decide.

Something needed to change again.

I decided it was time to slow down and focus. It was also time to figure out what I actually wanted to build.

I didn't need another business opportunity. I needed to slow down, stop chasing everything, and figure out what I actually wanted to build.

Why I Decided to Return to Online Writing

After spending several months exploring different ideas, I started thinking about online writing again.

I'd been interested in writing for a while.

And the more I thought about it, the more appealing it became.

Unlike running an AI automation community, writing would let me explore different subjects at my own pace without keeping up with every new AI tool.

Instead, I could focus on sharing what I discovered and learned. That eventually led me back to Ship 30 for 30, an online writing program that helps people build a consistent writing practice.

One thing I liked about the approach is writing short, focused essays; instead of trying to write long articles about everything I knew, I could start with one specific idea, question, problem, or lesson from my own experience.

I could develop that idea into an atomic essay and eventually expand it into something longer.

That approach appealed to me because it made writing feel manageable.

I didn't need to have everything figured out before I started.

I could write about what I was learning.

I could share my experiences.

And I could explore subjects that genuinely interested me.

For example, I'm still interested in AI and programming.

After years away from professional software development, I know what it's like to see how much technology has changed and wonder where to begin again.

I also know I'm not the only person in that situation.

Plenty of people in their 40s, 50s, and beyond have technical backgrounds but feel like the industry has moved on without them.

Others are complete beginners who want to learn programming but don't know how AI changes the learning process.

These are subjects I can relate to and want to explore through writing.

Instead of trying to position myself as someone who has mastered every new AI technology, I can share the process of learning and figuring things out.

I can write about the challenges.

I can share what works and what doesn't.

And I can help people who are dealing with some of the same questions.

For me, that feels like a better direction.

It doesn't mean writing will always be easy.

And it doesn't mean I've suddenly figured out exactly what my online business will look like.

But writing gives me a way to create consistently while I keep learning.

More importantly, it gives me something I can focus on without feeling like I need to chase every new opportunity.

Five Lessons I Learned From Walking Away

Looking back, shutting down AI Builder Lab taught me more than I expected.

I didn't see all of these lessons immediately. Some became clear only after I'd taken time away and started thinking about what I wanted to do next.

Here are five things I've taken from the experience.

1. Just because something is growing doesn't mean it's right for you.

When I decided to shut down my community, I was still gaining members.

From the outside, that might seem like a reason to keep going.

But growth doesn't tell the whole story.

A business can attract customers and still be something you don't enjoy operating.

I've learned that I need to consider more than whether an idea has the potential to make money.

I also need to ask whether I want to spend my time doing the work required to make it successful.

That's an important distinction.

Before starting another business, I want to think more carefully about what the day-to-day responsibilities will look like.

Because those are the things I'll actually be spending my time doing.

2. You don't have to keep up with everything.

This was one of my biggest mistakes.

I felt like I needed to stay informed about every new AI tool, feature, and development.

But that's an impossible standard.

Something new will always emerge.

And in a rapidly changing industry like AI, trying to keep up with everything can quickly become overwhelming.

I've realized I don't need to know everything.

I need to know enough about what matters for the projects I'm working on.

If I'm learning Python, I can focus on Python.

If I'm building a small AI-powered tool, I can learn what I need to complete that project.

And if a new AI tool comes along that doesn't help me accomplish my current goals, I don't need to stop everything to explore it.

The goal isn't to know every tool. It's to know which tools are worth my attention.

3. Shiny object syndrome usually gets worse when you lack direction.

Without a clear goal, almost every new opportunity looks interesting.

That's what happened after I shut down my community.

I watched videos about different business models, explored new technologies, and considered multiple directions.

But because I hadn't decided what I really wanted, I kept jumping from one idea to another.

I've learned that having a clear direction makes it easier to ignore distractions.

It doesn't mean I have to stop being curious.

It means I need to be more selective about what I spend my time learning.

Not every interesting opportunity needs to become my next project.

4. Walking away doesn't mean starting over from zero.

At first, I wondered whether the time I'd spent building AI Builder Lab had been wasted.

But the more I thought about it, the more I realized that wasn't true.

I still had the knowledge I'd gained from learning AI automation.

I still had the experience of creating and running an online community.

And I understood better what I enjoyed and what I didn't.

Those lessons didn't disappear when I shut down the community.

I can carry them into whatever I decide to build next.

Sometimes a project doesn't work out the way you expected, but that doesn't mean you got nothing from it.

5. Slowing down can be more productive than constantly chasing the next thing.

For a long time, I associated making progress with doing more and learning more tools.

I explored more opportunities and started more projects.

But constantly moving from one thing to another wasn't helping me make meaningful progress.

It was leaving me overwhelmed and unfocused. Stepping away gave me time to think about what I actually wanted.

Writing has given me a reason to focus on something specific. I also don’t need to move at the same pace as everyone else or jump on every trend.

And I don't need to turn every interest into a business opportunity.

Sometimes the best thing I can do is slow down, choose one direction, and give myself enough time to see where it leads.

Sometimes Walking Away Is How You Move Forward

I’d spent months trying to build something, and I was about to walk away without knowing what would come next.

Part of me felt relieved. Another part wondered whether I was making a mistake.

Several months later, I began to see the experience differently.

I don’t view shutting down the community as a declaration of failure. It helped me recognize what wasn’t working for me.

I was trying to keep up with too many things and putting too much pressure on myself. I was also building a business that didn’t fit the way I wanted to work.

The break I took didn’t solve those problems right away. In fact, I spent several months falling back into old habits, watching YouTube videos, and chasing new ideas.

But that experience eventually helped me recognize something needed to change.

I needed less noise and more focus. That’s part of why I’ve returned to online writing.

I’m still figuring out what I want to build and working on becoming more consistent and focused.

But I’m approaching things differently now.

Instead of trying to keep up with everything happening in AI, I want to concentrate on learning what matters to me and sharing that knowledge through writing.

I want to give myself time to develop something meaningful. Instead of judging every project solely by whether it succeeds, I want to focus on what I'm learning along the way.

I want to pay more attention to what I’m learning.

I don’t need to chase every opportunity that comes along. But I need to have the courage to walk away from something that no longer fits.

And sometimes the most important progress you make happens when you finally decide to slow down.

The day I decided to shut down my community, I told myself I was done

I will never forget where 1 was when I decided I was ready to shut my community down.

I was sitting in a sunroom where I live. It was a nice, cool day. And although I was gaining new members, it didn't feel right anymore. AI technology was moving so fast that I began to feel it was impossible to keep up.

So, after a few weeks, I decided it was time to shut it down and take a break.

I shut down my community and took a few months off from online business

It was a tough call but necessary.

It felt too much like a job. I stopped working on my community and began pulling away from building an online business. At the time, it felt good. I was relieved and started to relax and feel less stressed. I would be lying if I didn't feel some regret about not continuing.

But after a few months of watching YouTube videos, I fell into the shiny object syndrome trap again.

It took me a few months to see that decision as an act of courage, and not a declaration of failure

I did second-guess my decision for a few weeks afterward.

But I quickly got over it after watching YouTube videos on all things AI. It took me a few weeks of slowing down and pulling back to realize it wasn't just shiny object syndrome causing my problems, but a lack of focus and shiny object syndrome. This led me back to online writing and the online writing program, Ship 30 for 30.

It was time for me to slow down and take my time.

Unsure where to start setting up your coding environment?

Here are three tips to help get you started:

- Choose one programming language based on what you want to build

- Choose one AI assistant that helps you learn, not just generate code

- Choose a simple place to write and run your code

That's enough to get you started.

Here's what I believe about modern coding with AI:

You don't have to master programming before you can build something useful.

You can learn the fundamentals while creating small projects with AI as your assistant.

Three reasons why you should explore only one general-purpose AI assistant:

- They can help with writing, research, brainstorming, and learning.

- They offer an accessible starting point.

- Their capabilities overlap, so many skills are transferable to another.

The goal is to learn how to work with AI, not become an expert in every AI tool.

Here are three ways to learn ONE AI tool:

1. Start with a general-purpose AI assistant.

2. Learn transferable AI skills instead of memorizing a tool's features.

3. Take seven days to test a tool before committing.

The idea is to get comfortable with one AI assistant.

Maybe the goal isn't to catch up with everything happening in AI.

Maybe the goal is simply to learn how to use it to accomplish something useful.

3 Realistic AI Learning Goals Professionals Over 40 Can Achieve in Their First 30 Days

One of the biggest obstacles professionals over 40 face when learning artificial intelligence isn't necessarily the technology itself.

It's the belief that they're already too far behind.

You are not alone.

I feel the same way. I'm over 40, and sometimes I wonder if I've already fallen too far behind to keep up with AI.

AI tools are evolving quickly, and unfamiliar terminology can feel overwhelming. Add to that the fact that younger people seem more comfortable experimenting with these new technologies.

Every time I start to get comfortable with one AI tool, another one comes along, or the one I'm using gets a major update. New models, AI agents, automation platforms, and unfamiliar terminology make it seem like there's always something else I should be learning.

And when I see people building applications, automating their businesses, or accomplishing incredible things with AI, I can't help but wonder whether I've missed the boat.

But I'm beginning to realize something.

Maybe the goal isn't to catch up with everything happening in AI. Maybe the goal is simply to learn how to use it to accomplish something useful.

We don't need to become AI engineers, learn Python, or understand every new technology to start benefiting from AI.

We can start small, practice consistently, and build confidence through real-world experience.

You aren't starting from zero. You're bringing years of experience to a new technology. Your job in the first 30 days isn't to become an expert. It's to learn how to apply that experience with AI.

Here are three realistic AI learning goals professionals over 40 can work toward in their first 30 days:

1. Become comfortable using one AI tool

Many AI beginners try to learn everything at once.

I make this mistake too. We hear about ChatGPT, Claude, Gemini, AI agents, automation platforms, and countless other tools. Instead of making progress, we experience information overload and get overwhelmed.

I don't want to spend weeks learning something that will be obsolete tomorrow. You probably don't either.

You don't need to predict which AI tools will dominate the future. You need to choose one that solves today's problems while building skills you can transfer to other tools tomorrow.

Here are five steps you can follow to choose ONE tool:

Start with a general-purpose AI assistant, not a specialized tool.

Choose your first AI tool based on tasks you already perform.

Learn transferable AI skills instead of memorizing a tool's features.

Give yourself seven days to test a tool before committing.

Judge an AI tool by the results it helps you achieve, not its popularity.

The idea is to get comfortable with one AI assistant before exploring others.

General-purpose AI assistants let beginners explore several practical uses without learning a different app for every task.

Here are three reasons why you should commit to and explore only one general-purpose AI assistant:

General-purpose assistants can help with writing, research, brainstorming, planning, and learning.

They offer an accessible starting point without programming or automation experience.

Their capabilities overlap, so many skills learned in one transfer to another.

The goal is to learn how to work with AI, not become an expert in every available AI tool or product.

Confidence will come from using AI repeatedly, not from memorizing technical terminology.

2. Complete three real-world tasks using AI

Many AI beginners spend too much time consuming AI tutorials instead of actually using AI, including me.

You might be asking, "I understand that AI can help me work smarter, but what should I actually do with it tomorrow?"

You don't need to learn complicated AI techniques to start getting value from AI. Start with familiar tasks like improving written communication, researching something new, and planning or organizing a project.

Here are three practical tasks you can choose from:

Improve a professional email or written communication.

Research and learn something new using AI.

Plan or organize a small project using AI.

You will start to trust your ability to use AI when you see it help you accomplish something meaningful.

Completing these three tasks won't make us experts, but that's not the goal.

The goal is to become comfortable enough with AI that we no longer need a tutorial every time we want to use it.

Once we've used AI successfully for a few different tasks, the next step is to identify something we do regularly and turn it into a repeatable process.

That's where AI can start becoming more than something we occasionally experiment with. It can become a useful part of how we work.

You don't build confidence with AI by learning everything it can do. You build confidence by using it to accomplish something you already know how to do.

3. Build one repeatable AI workflow

When we first start using AI, it's easy to treat it like a search engine.

We ask a question, get an answer, and move on.

I've done this and still do sometimes.

But if we want AI to become genuinely useful in our professional lives, we need to move beyond occasional conversations and start using it for tasks we perform regularly.

The good news is that we don't need complicated automations, AI agents, or programming experience to do this.

We can start by identifying one recurring task, creating a simple process, and improving it each time we use it.

Here are five practical steps you can follow to build a workflow:

Identify one recurring task you already perform every week.

Break the task into a simple, repeatable process.

Create and save a reusable prompt for the task.

Repeat the workflow and refine it based on the results.

Measure whether the workflow actually improves your work.

You don't need to automate your entire professional life. Start by making one recurring task easier, and build from there.

Your first 30 days should be about confidence, not expertise.

3 technical milestones for an IT Pro's first 90 days back in tech after a long career gap

I'm a former IT professional with more than a decade-long career gap.

These milestones are a great start for someone like me with a long career gap in IT.

These milestones are what I'm striving for.

1. Refresh your coding fundamentals

Refresh your coding fundamentals by choosing one primary language, like Python.

If you are over 40 like me and returning to IT after a long career gap, it's tempting to jump straight into AI coding assistants, modern frameworks, and the latest development tools.

There is a better way.

Refresh the coding fundamentals you already learned years ago.

You don't need to start from scratch or spend weeks and months relearning everything. You need to rebuild the foundation you can use with today's tools.

Here are three reasons why:

Your coding fundamentals are still relevant, even if the tools have changed: Programming languages, frameworks, and development tools have evolved, but many core programming concepts remain the same. For example, variables, loops, conditionals, functions, and data structures are still essential. Problem-solving and logical thinking also transfer across programming languages.

You need to understand the code AI generates: AI coding tools can write code quickly, but you still need enough programming knowledge to determine whether it's correct, secure, and reliable. For example, AI-generated code might contain bugs, incorrect assumptions, and security vulnerabilities. If you understand programming fundamentals, you can spot when something doesn't make sense.

Refreshing fundamentals helps you rebuild confidence through small wins: After a decade away from programming, my biggest challenge isn't learning new technology. It's rebuilding confidence in my ability to write code.

And remember...

You aren't starting over. You're updating skills you already have.

You don't need to master every modern framework. You need a strong enough foundation to learn and adapt.

The goal isn't to become an expert in 30-90 days. It's to rebuild enough foundational knowledge to write simple programs, understand code, troubleshoot problems, and eventually build useful software with AI assistance.

Here are five practical steps to follow:

Choose one programming language to focus on

Refresh the core programming concepts you actually need

Practice coding every day with small exercises

Relearn how to debug and troubleshoot code

Combine your refreshed fundamentals into one small working program

Once you refresh your coding fundamentals, you're ready to build a real software project.

2. Build your first modern software project

It's time to put those refreshed skills to work.

Choose a project small enough in scope to finish.

It doesn't need to be complicated. The goal is to finish something useful, learn how modern development works, and prove to yourself that you can still build software.

Here are five practical steps for going from reviewing Python fundamentals to completing our first modern software project:

Choose one small project that solves a real problem: Build a website availability monitor that checks five websites, records their HTTP status codes and response times, and saves the results to a CSV file.

Break your project into small, manageable tasks: Before writing code, identify the individual tasks your program needs to perform. Experienced programmers may remember gathering requirements, writing pseudocode, or designing program logic before coding.

Learn and use a few modern development tools: Building a modern software project doesn't require mastering an entire new development ecosystem. A simple Python development setup might be Python, VS Code, Git, GitHub, a Python virtual environment, and an AI coding assistant. Start with Python, a code editor, Git, and GitHub.

Build the simplest working version and test it as you go: Focus on getting a minimum viable product (MVP) version working before making it more complicated. Get the MVP working, test normal and failure cases, and resist adding features that aren't essential.

Finish, document, and share your completed project: A completed, documented project provides more evidence of your skills than several unfinished programs. It doesn't have to be perfect. It means you've built something that works, and you understand how it operates.

You've refreshed your coding skills and built your first project.

Now it's time to choose one marketable technical skill to develop.

3. Develop one marketable technical skill

After being away from technology for a decade or more, deciding which technical skill to develop can feel overwhelming.

I feel overwhelmed.

More tools and technologies exist than ever before.

Here's the important distinction, though:

You don't need to learn every new technology. You only need to learn those worth developing.

Here are 5 reasons to consider when choosing which technical skill is worth developing:

Choose a skill that builds on your previous IT experience. Your previous technical experience is an advantage, even if the tools you once used have changed.

Choose a skill that solves real business problems. A technical skill becomes marketable when it helps an employer or client solve a meaningful problem.

Look for skills with evidence of current market demand. Look for evidence that employers and clients actually need it.

Choose a skill you can practice by building real projects. A technical skill is more useful when you can demonstrate it through completed work, not just claim you've learned it.

Choose a skill that can grow into other technical opportunities. Your first marketable skill should be a starting point, not a permanent limitation.

You don't need to catch up on every technology you've missed over the last decade.

You need to identify one valuable skill, build on what you already know, and learn to solve real problems with it.

Choose one skill to start with, like Python automation.

You can learn to automate file processing, analyze logs, and generate reports.

What should you realistically be able to do after 90 days back in IT?

Returning to IT after a decade or more can feel like starting from scratch.

Technology has changed. AI coding assistants are everywhere. Development tools and frameworks also differ from what you remember.

But 90 days isn't about catching up on everything you've missed. It's about rebuilding your skills, gaining confidence, and proving you can still solve technical problems.

By the end of those first 90 days, aim to have a working foundation, a completed project, and one technical skill you can keep developing.

Here are three realistic outcomes to work toward, and the ones I'm striving for too:

Write and understand basic code without relying entirely on tutorials or AI.

Complete one small, working software project.

Rebuild enough confidence to know what comes next.

I am in my first 90 days back in IT too, and I find it overwhelming to think about sometimes. I'm working on a 90-day plan, starting with the first 30 days of refreshing my coding skills.

Key takeaway: The goal of your first 90 days back in IT isn't to become the programmer you once were 10+ years ago. It's to become a capable, confident professional who can apply existing experience to today's technology.

3 Coding milestones for learning to code with AI:

• Understand and modify basic programming code

• Build and debug your first small program

• Build, customize, and share a complete mini-project

You don't need to become an expert in 30 days.

3 Coding milestones for learning to code with AI:

• Understand and modify basic programming code

• Build and debug your first small program

• Build, customize, and share a complete mini-project

You don't need to become an expert in 30 days. https://t.co/c2YELS8wJF

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