AI-assisted planning, architecture exploration, technology evaluation, design, and task breakdown before coding

New Jira and Teamwork Graph capabilities help engineering teams plan, assign, govern, and measure work across humans and AI agents.

Everyone is afraid AI will replace developers.

They're asking the wrong question.

The right question is: which parts of "developer" get replaced, and which parts become more valuable?

AI is very good at writing code when the problem is well-defined.

It is not good at deciding what the problem actually is.

It can give you ten ways to build a feature.

It cannot tell you whether that feature should exist.

What's getting replaced is the part of the job that was always the least interesting anyway: writing boilerplate, looking up syntax, building the same CRUD screen for the hundredth time.

What's becoming more valuable is the part that was always hardest to teach: understanding the actual problem, talking to the people who have it, and making judgment calls with incomplete information.

I use AI every day now. It writes a meaningful share of our code.

But the decisions about what to build, why, and for whom.
That's still entirely human, and I don't see that changing.

The developers who'll struggle aren't the ones who use AI.

They're the ones whose entire value was "I can write this code" and nothing else.

AI didn't make me a 10x developer

It removed hours of boring work.

Now I use it to:
• Design APIs
• Generate boilerplate
• Review code
• Find edge cases
• Write tests
• Create documentation

The biggest productivity gain isn't coding.

It's reducing context switching.

How are you using AI in development?

If you've used AI to write code for more than a week, you've hit this wall:

You ask for a component. It gives you something that works — technically — but looks nothing like your actual codebase. Wrong naming conventions. Wrong library usage. Wrong patterns. You spend 20 minutes rewriting the thing you asked it to write.

The output isn't bad. It's just generic. And generic doesn't ship.

Here's how I stopped getting boilerplate and started getting code I can actually use.

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.