Over the past few months, I’ve been creating projects/applications with AI-powered coding assistants, and the experience has been nothing short of transformative. What started as curiosity has evolved into a fundamental shift in how I approach software development. Here’s what I learned about the capabilities, limitations, and best practices of coding with AI.

The 7 AI coding skills I use every single day.

(All free to download):

If you spend any time in AI circles online, it's easy to come away thinking you need hundreds of skills, dozens of plugins, and an ever-growing stack of MCP servers to be productive with coding agents.

I've come to believe the opposite.

The engineers I see shipping the most consistent, high-quality work tend to use a small number of well-designed skills that map to the workflows they repeat every day. Planning, implementing, reviewing. That's most of the job.

I've spent the last couple of years building, breaking, and rebuilding my own toolkit. It's settled into just a few skills that I genuinely use every day across professional projects, and that I'd happily defend as the only ones most engineers need.

I just put together a full video walking through all seven, with live demos in Codex (though they work fine in Claude Code or any other agent).

I show how I use each one, why it earns its place, and the pattern underneath them that I think matters more than the list itself.

https://lnkd.in/e_r62kya

Every skill is free and linked in the description so you can grab them and try them yourself.

If you've got a skill you swear by that you think I'm missing, let me know in the comments.

The best part of working in public is the steady stream of better ideas coming back from people who've solved problems I haven't noticed yet.

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Now in early beta for SuperGrok Heavy subscribers — Grok Build is a new coding agent that runs right from your terminal.

Today we're launching an early beta of Grok Build, a powerful new coding agent and CLI for professional software engineering and complex coding work.

A practical look at Anthropic’s agentic coding tool — what it actually does, how it changed my workflow, and whether it’s worth your time.

I’m a Principal Developer and I haven’t written a line of code in a year.

That’s a strange sentence to write.

A year ago, I was still deep in C#, TypeScript, APIs, infrastructure, architecture reviews, debugging production systems, Terraform, and CI/CD pipelines.

Today?

I mostly describe systems.
I talk to AI.
I architect with AI.
I review with AI.
I direct, refine, test, challenge, and iterate with AI.

But physically typing code?

Almost never.

The last thing I manually “coded” was tweaking a bit of Terraform. Even that now feels one voice command away from disappearing entirely.

And honestly, it’s unsettling.

I genuinely feel like an accountant in 1863 who’s just been handed a MacBook Pro and a subscription to Xero.

Not because it’s impossible to comprehend.

Because within minutes you realise entire industries are about to change around it.

And then the terrifying thought arrives:

What could somebody from that era have built if they’d truly understood the tool they were holding?

That’s the uncomfortable part about the current AI wave.

Not the hype.

Not the demos.

The speed.

Because we’re rapidly moving toward a world where a non-technical person says:

“I want a CRM system that connects warehouse operations, customer service, complaints, sales, marketing, IT, security testing, and technical teams” and I want it to solve operational problems.

And increasingly, the answer is no longer:

“That will take a team of developers 18 months.”

The answer is:

“Okay.”

That’s the shift.

Not years away.

Months away if not days.

Software development itself is becoming abstracted.

The value is moving higher up the stack:

Understanding systems
Understanding businesses
Understanding people

I’m obsessed with AI because I understand what it can deliver.

The closer you are to the technology, the less theoretical it feels.

I sit there sometimes thinking:

What do you even tell your children to learn now?

What skills still compound?

What does society look like in 18 months if this pace continues?

For decades we built society around knowledge accumulation.

Go to university.
Build expertise.
Become specialised.

But what happens when intelligence itself becomes massively accessible?

What happens when execution collapses from years into days?

It’s beginning to feel like the bottleneck is no longer software development.

Delivery is rapidly becoming commoditised.

The people who win over the next few years probably won’t be the people who produce the most output manually.

They’ll be the people who can identify valuable problems and direct intelligence effectively.

That’s partly why I’m so focused on AI now.

Because it feels inevitable.

And honestly, the biggest challenge no longer feels technical.

The challenge is figuring out where to apply all of this capability before the rest of the world catches up.

Because for the first time in my career, I’m not sure where the ceiling is anymore.

And I’m not sure anybody else does either.

To solve this new problem, we built a system called Symphony. Symphony is an agent orchestrator that turns a project-management board like Linear into a control plane for coding agents. Every open task gets an agent, agents run continuously, and humans review the results.

This post explains how we created Symphony—resulting in a 500% increase in landed pull requests on some teams—and how to use it to turn your own issue tracker into an always-on agent orchestrator.

Over the past year, I changed my perspective on AI in software development. I used to be skeptical, but after watching Burke Holland's content on Copilot agents, I realized that I had to give it another try. From using AI as a simple code completion tool, to a tool to generate tests or simple isolated enhancements and smaller tasks, I started to see it as a system that's capable of doing much more. Fast forward to today, I'm using it more and more to create new features from scratch.

Over the last month I used Agentic AI to rewrite a project entirely from scratch, and the results are amazing. The speed boost is incredible, and the quality of the code is good, not perfect, but good enough in my opinion. In this blog post, I want to share my experience and thoughts on AI in software development, and why I think it's a game-changer for software development.

The Lie We All Started With

When AI tools first showed up, I used them the same way most engineers did.

Generate code. Fix bugs. Write boilerplate.

It felt impressive for about a week.

Then it started feeling shallow.

Because code was never the hardest part of engineering.

Thinking was.

Designing systems. Making tradeoffs. Understanding consequences before they happen.

That is where most mistakes happen. And where most time is lost.

That is when I changed how I used AI.

Not as a coding assistant.

But as a thinking partner.

Where AI Actually Changed My Workflow

My daily work did not become easier.

It became sharper.

Instead of asking AI to solve problems for me, I started using it to explore problems with me.

That shift changed everything.
...

At first, I thought AI would mostly help me write code faster. That’s how it’s usually presented, as a way to speed up implementation or generate code automatically.

But after using it daily for a while, I realized that this is not where it actually brings the most value.

AI doesn’t really change how fast I type code. It changes how I approach problems, especially the parts of the job that are slow, repetitive, or mentally expensive.

The way I think about it today is simple: AI behaves like a very fast junior engineer.

It can generate ideas quickly, suggest implementations, and help explore possibilities. But it doesn’t truly understand the system, the business context, or the consequences of a decision.

Once I started treating it this way, it became much more useful — and much less dangerous.

The discourse around AI software development is stuck between two extremes: people shipping toy apps in a weekend, and people insisting these tools are useless in real codebases. My experience has been neither.

AI doesn’t replace software engineering judgment. It amplifies it best when you use a disciplined workflow built around context, planning, staged execution, and strong verification.

I’m a Staff Software Engineer with 23 years of experience, and this is the workflow I use to ship production software with AI in 2026. I will walk you through how I am leveraging AI tooling to deliver new features into production software products.

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